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Informatica LLC provides data integration software and services. The Company's software allows its clients to access, integrate and trust all their information assets. Informatica's infrastructure software categories include data integration, cloud computing, complex event processing, application information lifecycle management, data quality, B2B Data Exchange, and others.

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When Jara Helmes was a toddler walking with stiff, heavy leg braces, her father made her a lighter, softer pair 3D-printed in her favorite color, pink. When

New mix-and-match computer accessories give people with … – Microsoft

Summary Informatica Inc. provides software and services for data integration and management in companies worldwide. The company has transformed into a cloud-based SaaS company, offering various data management solutions.

Informatica Ups Guidance But Valuation Looks Full

In this article, I will delve into whether artificial intelligence (AI), particularly the emergence of tools like generative AI (GenAI), will replace classical software outsourcing as we know it.

AI vs software outsourcing: An opportunity or a threat?

Our team of computer vision developers develops custom image and video analysis software for machine vision and computer vision systems. We build computer vision software that can perform multiple tasks, including face analysis, real-time gesture and ...

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The Visual Computing Group at Microsoft Research Asia consists of an elite team of researchers whose expertise spans the spectrum of research topics in computer vision, from mathematical theory to practical applications, from physical systems to software ...

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MONTREAL — The Canadian National Railway computer outage that disrupted GO Transit and VIA Rail Canada trains on Tuesday was caused by a software upgrade, CN said on Wednesday. CN spokesman Jonathan Abecassis told CTV News that the railroad had ...

Software upgrade led to CN computer outage

Our B.A. in Computer Science: Software Project Management addresses a growing need in industry to manage the finances and personnel of complex software development projects. Computer Science majors who graduate and start out as programmers in larger ...

Software Project Management Major

Authors:
(1) Ankur Nath, Department of Computer Science and Engineering, Texas A&M University;
(2) Alan Kuhnle, Department of Computer Science and Engineering, Texas A&M University. Table of Links Abstract & Introduction Related work Evaluation for Max-Cut Evaluation for SAT Summary and Outlook, References Supplementary Materials 5 SUMMARY and OUTLOOK Through our empirical evaluations, our goal is to promote an insightful comparison within the research focusing on the intersection of combinatorial optimization and machine learning. In order to provide the research community with valuable guidance, we believe it is imperative to communicate both the strengths and weaknesses of the proposed approaches. Poor instances and baseline selection may give the wrong impression about the performance of learned heuristics. Specifically, it is important to articulate the degree of improvement achieved through integrating classical heuristics with deep learning architectures and conduct a thorough comparison with classical heuristics. This will aid in elucidating the degree to which deep learning architectures enhance integrated heuristics. It assists in ascertaining whether the integration endeavor is justified and warrants the allocation of computational resources, time, and investment necessary for integrating deep learning with classical heuristics. References Albert, R. and Barab´asi, A.-L. (2002). Statistical mechanics of complex networks. Reviews of modern physics, 74(1):47. Barrett, T., Clements, W., Foerster, J., and Lvovsky, A. (2020). Exploratory combinatorial optimization with reinforcement learning. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pages 3243–3250. Barrett, T. D., Parsonson, C. W., and Laterre, A. (2022). Learning to solve combinatorial graph partitioning problems via efficient exploration. arXiv preprint arXiv:2205.14105. Battiti, R. and Protasi, M. (1997). Reactive search, a history-sensitive heuristic for max-sat. Journal of Experimental Algorithmics (JEA), 2:2–es. Battiti, R. and Tecchiolli, G. (1994). The reactive tabu search. ORSA journal on computing, 6(2):126–140. Bello, I., Pham, H., Le, Q. V., Norouzi, M., and Bengio, S. (2016). Neural combinatorial optimization with reinforcement learning. arXiv preprint arXiv:1611.09940. Bengio, Y., Lodi, A., and Prouvost, A. (2021). Machine learning for combinatorial optimization: a methodological tour d’horizon. European Journal of Operational Research, 290(2):405–421. Benlic, U. and Hao, J.-K. (2013). Breakout local search for the max-cutproblem. Engineering Applications of Artificial Intelligence, 26(3):1162–1173. Boettcher, S. and Percus, A. G. (2001). Extremal optimization for graph partitioning. Physical Review E, 64(2):026114. CPLEX (2023). Cplex. https://www.ibm.com/products/ilog-cplexoptimization-studio/cplex-optimizer. Dong, Y., Goldberg, A. V., Noe, A., Parotsidis, N., Resende, M. G., and Spaen, Q. (2021). New instances for maximum weight independent set from a vehicle routing application. In Operations Research Forum, volume 2, pages 1–6. Springer. E´en, N. and S¨orensson, N. (2003). An extensible satsolver. In International conference on theory and applications of satisfiability testing, pages 502–518. Springer. Elsokkary, N., Khan, F. S., La Torre, D., Humble, T. S., and Gottlieb, J. (2017). Financial portfolio management using d-wave quantum optimizer: The case of abu dhabi securities exchange. Technical report, Oak Ridge National Lab.(ORNL), Oak Ridge, TN (United States). Erd˝os, P., R´enyi, A., et al. (1960). On the evolution of random graphs. Publ. math. inst. hung. acad. sci, 5(1):17–60. Festa, P. and Resende, M. G. (2009). Hybrid grasp heuristics. In Foundations of Computational Intelligence Volume 3: Global Optimization, pages 75–100. Springer. Flint, A. and Blaschko, M. (2012). Perceptron learning of sat. Advances in neural information processing systems, 25. Fujishige, S. (2005). Submodular functions and optimization. Elsevier. Ganesh, V., Singh, R., Near, J. P., and Rinard, M. (2009). Avatarsat: An auto-tuning boolean sat solver. Glover, F. (1990). Tabu search: A tutorial. Interfaces, 20(4):74–94. Goemans, M. X. and Williamson, D. P. (1995). Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming. Journal of the ACM (JACM), 42(6):1115–1145. Grozea, C. and Popescu, M. (2014). Can machine learning learn a decision oracle for np problems? a test on sat. Fundamenta Informaticae, 131(3- 4):441–450. Haim, S. and Walsh, T. (2009). Restart strategy selection using machine learning techniques. In International Conference on Theory and Applications of Satisfiability Testing, pages 312–325. Springer. Helmberg, C. and Rendl, F. (2000). A spectral bundle method for semidefinite programming. SIAM Journal on Optimization, 10(3):673–696. Hoos, H. H. et al. (2002). An adaptive noise mechanism for walksat. In AAAI/IAAI, pages 655–660. Jaszczur, S., Luszczyk, M., and Michalewski, H. (2020). Neural heuristics for sat solving. arXiv preprint arXiv:2005.13406. Karp, R. M. (1972). Reducibility among Combinatorial Problems, pages 85–103. Springer US, Boston, MA. Khalil, E., Dai, H., Zhang, Y., Dilkina, B., and Song, L. (2017). Learning combinatorial optimization algorithms over graphs. Advances in neural information processing systems, 30. KhudaBukhsh, A. R., Xu, L., Hoos, H. H., and LeytonBrown, K. (2016). Satenstein: Automatically building local search sat solvers from components. Artificial Intelligence, 232:20–42. Kirkpatrick, S., Gelatt Jr, C. D., and Vecchi, M. P. (1983). Optimization by simulated annealing. science, 220(4598):671–680. Kurin, V., Godil, S., Whiteson, S., and Catanzaro, B. (2020). Can q-learning with graph networks learn a generalizable branching heuristic for a sat solver? Advances in Neural Information Processing Systems, 33:9608–9621. Lauria, M., Elffers, J., Nordstr¨om, J., and Vinyals, M. (2017). Cnfgen: A generator of crafted benchmarks. In Theory and Applications of Satisfiability Testing–SAT 2017: 20th International Conference, Melbourne, VIC, Australia, August 28–September 1, 2017, Proceedings 20, pages 464–473. Springer. Lederman, G., Rabe, M., Seshia, S., and Lee, E. A. (2019). Learning heuristics for quantified boolean formulas through reinforcement learning. In International Conference on LearningRepresentations. Leleu, T., Yamamoto, Y., McMahon, P. L., and Aihara, K. (2019). Destabilization of local minima in analog spin systems by correction of amplitude heterogeneity. Physical review letters,122(4):040607. Li, C. M., Wei, W., and Zhang, H. (2007). Combining adaptive noise and look-ahead in local search for sat. In Theory and Applications of Satisfiability Testing– SAT 2007: 10th International Conference, Lisbon, Portugal, May 28-31, 2007. Proceedings 10, pages 121–133. Springer. Liang, J. H., Ganesh, V., Poupart, P., and Czarnecki, K. (2016). Learning rate based branching heuristic for sat solvers. In Theory and Applications of Satisfiability Testing–SAT 2016: 19th International Conference, Bordeaux, France, July 5-8, 2016, Proceedings 19, pages 123–140. Springer. Manchanda, S., Mittal, A., Dhawan, A., Medya, S., Ranu, S., and Singh, A. (2019). Learning heuristics over large graphs via deep reinforcement learning. arXiv preprint arXiv:1903.03332. Mazure, B., Sais, L., and Gr´egoire, E. (1997). Tabu ´ search for sat. In Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence, pages 281–285. McAllester, D., Selman, B., Kautz, H., et al. (1997). Evidence for invariants in local search. In AAAI/IAAI, pages 321–326. Rhode Island, USA. Mitchell, D., Selman, B., Levesque, H., et al. (1992). Hard and easy distributions of sat problems. In Aaai, volume 92, pages 459–465. Perdomo-Ortiz, A., Dickson, N., Drew-Brook, M., Rose, G., and Aspuru-Guzik, A. (2012). Finding low-energy conformations of lattice protein models by quantum annealing. Scientific reports, 2(1):1–7. Selman, B., Kautz, H. A., Cohen, B., et al. (1993). Local search strategies for satisfiability testing. Cliques, coloring, and satisfiability, 26:521–532. Selman, B., Mitchell, D. G., and Levesque, H. J. (1996). Generating hard satisfiability problems. Artificial intelligence, 81(1-2):17–29. Tiunov, E. S., Ulanov, A. E., and Lvovsky, A. (2019). Annealing by simulating the coherent ising machine. Optics express, 27(7):10288–10295. Toenshoff, J., Ritzert, M., Wolf, H., and Grohe, M. (2019). Graph neural networks for maximum constraint satisfaction. Frontiers in artificial intelligence, 3:580607. Venturelli, D. and Kondratyev, A. (2019). Reverse quantum annealing approach to portfolio optimization problems. Quantum Machine Intelligence, 1(1- 2):17–30. Wolpert, D. H. and Macready, W. G. (1997). No free lunch theorems for optimization. IEEE transactions on evolutionary computation, 1(1):67–82. Ye, Y. (2003). The gset dataset. https://web.stanford.edu/ yyye/yyye/Gset/. Yolcu, E. and P´oczos, B. (2019). Learning local search heuristics for boolean satisfiability. Advances in Neural Information Processing Systems, 32. This paper is available on arxiv under CC 4.0 DEED license. Authors: (1) Ankur Nath, Department of Computer Science and Engineering, Texas A&M University; (2) Alan Kuhnle, Department of Computer Science and Engineering, Texas A&M University. Authors: Authors: (1) Ankur Nath, Department of Computer Science and Engineering, Texas A&M University; (2) Alan Kuhnle, Department of Computer Science and Engineering, Texas A&M University. Table of Links Abstract & Introduction Abstract & Introduction Related work Related work Evaluation for Max-Cut Evaluation for Max-Cut Evaluation for SAT Evaluation for SAT Summary and Outlook, References Summary and Outlook, References Supplementary Materials Supplementary Materials 5 SUMMARY and OUTLOOK Through our empirical evaluations, our goal is to promote an insightful comparison within the research focusing on the intersection of combinatorial optimization and machine learning. In order to provide the research community with valuable guidance, we believe it is imperative to communicate both the strengths and weaknesses of the proposed approaches. Poor instances and baseline selection may give the wrong impression about the performance of learned heuristics. Specifically, it is important to articulate the degree of improvement achieved through integrating classical heuristics with deep learning architectures and conduct a thorough comparison with classical heuristics. This will aid in elucidating the degree to which deep learning architectures enhance integrated heuristics. It assists in ascertaining whether the integration endeavor is justified and warrants the allocation of computational resources, time, and investment necessary for integrating deep learning with classical heuristics. References Albert, R. and Barab´asi, A.-L. (2002). Statistical mechanics of complex networks. Reviews of modern physics, 74(1):47. Barrett, T., Clements, W., Foerster, J., and Lvovsky, A. (2020). Exploratory combinatorial optimization with reinforcement learning. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pages 3243–3250. Barrett, T. D., Parsonson, C. W., and Laterre, A. (2022). Learning to solve combinatorial graph partitioning problems via efficient exploration. arXiv preprint arXiv:2205.14105. Battiti, R. and Protasi, M. (1997). Reactive search, a history-sensitive heuristic for max-sat. Journal of Experimental Algorithmics (JEA), 2:2–es. Battiti, R. and Tecchiolli, G. (1994). The reactive tabu search. ORSA journal on computing, 6(2):126–140. Bello, I., Pham, H., Le, Q. V., Norouzi, M., and Bengio, S. (2016). Neural combinatorial optimization with reinforcement learning. arXiv preprint arXiv:1611.09940. Bengio, Y., Lodi, A., and Prouvost, A. (2021). Machine learning for combinatorial optimization: a methodological tour d’horizon. European Journal of Operational Research, 290(2):405–421. Benlic, U. and Hao, J.-K. (2013). Breakout local search for the max-cutproblem. Engineering Applications of Artificial Intelligence, 26(3):1162–1173. Boettcher, S. and Percus, A. G. (2001). Extremal optimization for graph partitioning. Physical Review E, 64(2):026114. CPLEX (2023). Cplex. https://www.ibm.com/products/ilog-cplexoptimization-studio/cplex-optimizer. Dong, Y., Goldberg, A. V., Noe, A., Parotsidis, N., Resende, M. G., and Spaen, Q. (2021). New instances for maximum weight independent set from a vehicle routing application. In Operations Research Forum, volume 2, pages 1–6. Springer. E´en, N. and S¨orensson, N. (2003). An extensible satsolver. In International conference on theory and applications of satisfiability testing, pages 502–518. Springer. Elsokkary, N., Khan, F. S., La Torre, D., Humble, T. S., and Gottlieb, J. (2017). Financial portfolio management using d-wave quantum optimizer: The case of abu dhabi securities exchange. Technical report, Oak Ridge National Lab.(ORNL), Oak Ridge, TN (United States). Erd˝os, P., R´enyi, A., et al. (1960). On the evolution of random graphs. Publ. math. inst. hung. acad. sci, 5(1):17–60. Festa, P. and Resende, M. G. (2009). Hybrid grasp heuristics. In Foundations of Computational Intelligence Volume 3: Global Optimization, pages 75–100. Springer. Flint, A. and Blaschko, M. (2012). Perceptron learning of sat. Advances in neural information processing systems, 25. Fujishige, S. (2005). Submodular functions and optimization. Elsevier. Ganesh, V., Singh, R., Near, J. P., and Rinard, M. (2009). Avatarsat: An auto-tuning boolean sat solver. Glover, F. (1990). Tabu search: A tutorial. Interfaces, 20(4):74–94. Goemans, M. X. and Williamson, D. P. (1995). Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming. Journal of the ACM (JACM), 42(6):1115–1145. Grozea, C. and Popescu, M. (2014). Can machine learning learn a decision oracle for np problems? a test on sat. Fundamenta Informaticae, 131(3- 4):441–450. Haim, S. and Walsh, T. (2009). Restart strategy selection using machine learning techniques. In International Conference on Theory and Applications of Satisfiability Testing, pages 312–325. Springer. Helmberg, C. and Rendl, F. (2000). A spectral bundle method for semidefinite programming. SIAM Journal on Optimization, 10(3):673–696. Hoos, H. H. et al. (2002). An adaptive noise mechanism for walksat. In AAAI/IAAI, pages 655–660. Jaszczur, S., Luszczyk, M., and Michalewski, H. (2020). Neural heuristics for sat solving. arXiv preprint arXiv:2005.13406. Karp, R. M. (1972). Reducibility among Combinatorial Problems, pages 85–103. Springer US, Boston, MA. Khalil, E., Dai, H., Zhang, Y., Dilkina, B., and Song, L. (2017). Learning combinatorial optimization algorithms over graphs. Advances in neural information processing systems, 30. KhudaBukhsh, A. R., Xu, L., Hoos, H. H., and LeytonBrown, K. (2016). Satenstein: Automatically building local search sat solvers from components. Artificial Intelligence, 232:20–42. Kirkpatrick, S., Gelatt Jr, C. D., and Vecchi, M. P. (1983). Optimization by simulated annealing. science, 220(4598):671–680. Kurin, V., Godil, S., Whiteson, S., and Catanzaro, B. (2020). Can q-learning with graph networks learn a generalizable branching heuristic for a sat solver? Advances in Neural Information Processing Systems, 33:9608–9621. Lauria, M., Elffers, J., Nordstr¨om, J., and Vinyals, M. (2017). Cnfgen: A generator of crafted benchmarks. In Theory and Applications of Satisfiability Testing–SAT 2017: 20th International Conference, Melbourne, VIC, Australia, August 28–September 1, 2017, Proceedings 20, pages 464–473. Springer. Lederman, G., Rabe, M., Seshia, S., and Lee, E. A. (2019). Learning heuristics for quantified boolean formulas through reinforcement learning. In International Conference on LearningRepresentations. Leleu, T., Yamamoto, Y., McMahon, P. L., and Aihara, K. (2019). Destabilization of local minima in analog spin systems by correction of amplitude heterogeneity. Physical review letters,122(4):040607. Li, C. M., Wei, W., and Zhang, H. (2007). Combining adaptive noise and look-ahead in local search for sat. In Theory and Applications of Satisfiability Testing– SAT 2007: 10th International Conference, Lisbon, Portugal, May 28-31, 2007. Proceedings 10, pages 121–133. Springer. Liang, J. H., Ganesh, V., Poupart, P., and Czarnecki, K. (2016). Learning rate based branching heuristic for sat solvers. In Theory and Applications of Satisfiability Testing–SAT 2016: 19th International Conference, Bordeaux, France, July 5-8, 2016, Proceedings 19, pages 123–140. Springer. Manchanda, S., Mittal, A., Dhawan, A., Medya, S., Ranu, S., and Singh, A. (2019). Learning heuristics over large graphs via deep reinforcement learning. arXiv preprint arXiv:1903.03332. Mazure, B., Sais, L., and Gr´egoire, E. (1997). Tabu ´ search for sat. In Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence, pages 281–285. McAllester, D., Selman, B., Kautz, H., et al. (1997). Evidence for invariants in local search. In AAAI/IAAI, pages 321–326. Rhode Island, USA. Mitchell, D., Selman, B., Levesque, H., et al. (1992). Hard and easy distributions of sat problems. In Aaai, volume 92, pages 459–465. Perdomo-Ortiz, A., Dickson, N., Drew-Brook, M., Rose, G., and Aspuru-Guzik, A. (2012). Finding low-energy conformations of lattice protein models by quantum annealing. Scientific reports, 2(1):1–7. Selman, B., Kautz, H. A., Cohen, B., et al. (1993). Local search strategies for satisfiability testing. Cliques, coloring, and satisfiability, 26:521–532. Selman, B., Mitchell, D. G., and Levesque, H. J. (1996). Generating hard satisfiability problems. Artificial intelligence, 81(1-2):17–29. Tiunov, E. S., Ulanov, A. E., and Lvovsky, A. (2019). Annealing by simulating the coherent ising machine. Optics express, 27(7):10288–10295. Toenshoff, J., Ritzert, M., Wolf, H., and Grohe, M. (2019). Graph neural networks for maximum constraint satisfaction. Frontiers in artificial intelligence, 3:580607. Venturelli, D. and Kondratyev, A. (2019). Reverse quantum annealing approach to portfolio optimization problems. Quantum Machine Intelligence, 1(1- 2):17–30. Wolpert, D. H. and Macready, W. G. (1997). No free lunch theorems for optimization. IEEE transactions on evolutionary computation, 1(1):67–82. Ye, Y. (2003). The gset dataset. https://web.stanford.edu/ yyye/yyye/Gset/. Yolcu, E. and P´oczos, B. (2019). Learning local search heuristics for boolean satisfiability. Advances in Neural Information Processing Systems, 32. This paper is available on arxiv under CC 4.0 DEED license. This paper is available on arxiv under CC 4.0 DEED license. available on arxiv

Assessing the Justification for Integrating Deep Learning in Combinatorial Optimization

As we approach the second quarter of 2024, Artificial Intelligence (AI) is driving significant changes in the field of data engineering. Integrating data engineering with AI has led to the popularity of modern data integration and the expertise required. I want to highlight here how the rise of AI will change data engineering trends in 2024 compared to previous years. First thing first, a quick recap on traditional data engineering Let's quickly recap how the data engineering role is born and grows with the passage of time. Almost a decade ago, businesses realised the importance of data-driven decisions. That opened roles like with expertise in Microsoft SSIS/SSRS, Talend, Informatica, and other similar tools. However, with the emergence of social media applications, these traditional ETL development tools faced limitations in processing billions of records. The idea of distributed storage and processing came into the field of play, such as Spark and NoSQL databases. As a result, the demand for Hadoop/Spark has increased. Exciting Hadoop times ahead! BI/ETL Developers Big Data Developers In 2006, AWS launched its cloud services S3 and EC2, followed by Microsoft and Google launching Azure and Google Cloud in 2008. This led to an increase in the use of cloud technologies. The cloud technologies are based on a pay-as-you-use model that avoids buying expensive servers and operating system licenses. These cloud technologies have now been called "Modern Data Warehouses" since the union of Big Data and Cloud. In early 2010, r jobs rose in the market with the release of AWS EMR, etc.  Because now, each and every company wants to migrate from on-premise to the cloud. With the passage of time, modern cloud-based data warehousing solutions have become costly and inefficient in handling unstructured data, leading to the birth of file-based data lake solutions. Cloud Develope Different Variations of Data Engineers The Data Engineering Landscape is quite complex and vast. Engineers and businesses struggle to keep up with the latest tools and technologies to build and maintain a scalable data platform. Each business creates its definition of data engineers, and some require data engineers to focus primarily on creating pipelines. Some require software engineering and reverse engineering expertise or the ability to build KPI models as an analytics engineer along with different cloud-based specializations. This is the current state of Data Engineering variation and demand, but  this is not stopping here. I want to highlight some other trends that are rising this year. AI Meets Data Engineers Recently, I saw on LinkedIn that many positions are open for . They mostly wrote that the responsibilities are to build LLM Models or prepare the batch/streaming data for these AI-based models. This includes regular data engineering tasks to build pipelines for product feature enhancement and enable data-driven business with a centralized data warehouse or data lake solution. AI Data Engineers Quality Assurance Engineer Merge with Data Engineer As a Data Engineer by profession, I realise that a data team should have at least one Quality Assurance Engineer. The QA takes all the responsibility for testing data quality, as they used to do web or mobile application testing. That person is always up-to-date with business domain knowledge. While the engineers are mostly focusing on technical aspects. With the rising demand for Gen AI solutions like GitHub Codepilot and ChatGPT, there is no guarantee that their solutions are always reliable; hence, it increases data quality problems with their use in production. Quality is one of the biggest challenges for small to enterprise companies. You are definitely going to see a lot more job openings as in the coming days.  This role is going to be the next demanding job in the coming months. Data Quality Engineer Batch Data Processing Could Obsolete Soon Stream data integration and processing keep adding value daily for the business allowing them to make faster decisions and offer better product features. The CDC (Change Data Capture) is already taking place for batch pipeline integration, primarily for the main source database. However, Due to less demand for batch processing and the high output of stream processing architecture, it may slowly become obsolete or less required in the future. Lakehouse Architecture Will Remain The First Choice Hundreds and thousands of companies have already taken advantage of building Lakehouse Architecture. They moved from the costly “Modern cloud data warehouse“ to the file-based Lakehouse Architecture either using Delta Lake, Apache Hudi or Apache Iceberg open storage format, as its ability to manage large-scale data with ACID transactions and schema enforcement will remain the first choice for next some year(s) unless we see any other new tools or architecture in the market. The popularity of No Code Data Integration Tools Modern Data Integration Tools like Airbyte, Mage.ai, Stitch, and Fivetran simplify data integration and workflow and reduce the need for custom Python-based data pipeline development. These tools speed up the process of pipeline development, but I will come up with the cost of the complexity of debugging these tools if something breaks; it will not be very straightforward to fix it. On the other hand, expert engineers will be required to kick-start the pipeline integration in one click. I would like to see how long we can go to adapt these tools, no-code-based tools.

The Emerging Data Engineering Trends You Should Check Out In 2024

This paper is available on arxiv under CC BY-SA 4.0 DEED license. Authors: (1) Tomáš Jakl, Czech Academy of Sciences and Czech Technical University; (2) Dan Marsden, School of Computer Science University of Nottingham; (3) Nihil Shah, Department of Computer Science University of Oxford. Table of Links Abstract & Introduction Prelimenaries FVM Theorems for Positive Existential Fragments FVM Theorems for Counting Logic FVM Theorems for The Full Logic Abstract FVM Theorems for Products Adding Equality and Other Enrichment Conclusions, Acknowledgments & References Appendix A FVM theorems for coproducts Appendix B Proofs Omitted from Section III Appendix C Proofs Omitted from Section IV Appendix D Proofs Omitted from Section V Appendix E Proofs Omitted from Section VI Appendix F Proofs Omitted from Section VII VIII. CONCLUSION We presented a categorical approach to the composition method, and specifically Feferman–Vaught–Mostowski theorems. We exploit game comonads to encapsulate the logics and their model comparison games. Surprising connections to classical constructions in category theory, and especially the monad theory of bilinear maps emerged from this approach, cf. Section V-A. For finite model theorists, our work provides a novel highlevel account of many FVM theorems, abstracting away from individual logics and constructions. Furthermore, concrete instances of these theorems are verified purely semantically, by finding a suitable collection homomorphisms forming a Kleisli law satisfying (S2’), instead of the usual delicate verification that strategies of model comparison games compose. For game comonads, we provide a much needed tool that enables us to handle logical relationships between structures as they are transformed or viewed in terms of different logics. The FVM results in this paper, combined with judicious use of the techniques described in Section VII can be pushed significantly further. FVM theorems and the compositional method are of particular significance in the setting of monadic second-order (MSO) logic [49]. Exploiting the results we have presented for the composition method, a follow up paper will give a comonadic semantics for MSO, and develop a semantic account of Courcelle’s algorithmic meta-theorems [5]. The original theorem of Feferman–Vaught [3] is very flexible, including incorporating structure on the indices of families of models to be combined by an operation, as well as the models themselves. This aspect is currently outside the scope of our comonadic methods. An ad-hoc adaptation of our approach also gives a proof of the FVM theorem for free amalgamations, but this does not follow directly from our theorems. Developing these extensions is left to future work. The present work bears some resemblance to Turi and Plotkin’s bialgebraic semantics [50], a categorical model of structural operational semantics (SOS) [51]. Turi and Plotkin noticed that the SOS rules assigning behaviour to syntax could be abstracted as a certain distributive law λ. An algebra encodes the composition operations of the syntax, and a coalgebra the behaviour. If this pair is suitably compatible with λ, forming a so-called λ-bialgebra, then crucially bisimulation is a congruence with respect to the composition operations. Both bialgebraic semantics and our work presented in this paper give a categorical account of well-behaved composition operations, with the interaction between composition and observable behaviour mediated by some form of distributive law. There are also essential differences: bialgebraic semantics typically focuses on assigning behaviour to syntax, whereas FVM theorems encompass operations on models. Our FVM theorems are parametric in a choice of logic or observable behaviour, while the notion of bisimulation in bialgebraic semantics is fixed by the coalgebra signature functor. The natural presence of positive existential and counting quantifier variants of FVM theorems, and the incorporation of resource parameters, do not seem to have a direct analogue in bialgebraic semantics. The similarities are intriguing, and exploring the relationship between bialgebraic semantics and our approach to FVM theorems is left to future work. We would like to thank Clemens Berger for suggesting the connection with parametric adjoints and Nathanael Arkor for his suggestions on terminology. We are also grateful to the members of the EPSRC project “Resources and co-resources” for their feedback. Acknowledgements: REFERENCES [1] L. Libkin, Elements of finite model theory. Springer, 2004, vol. 41. [2] A. Mostowski, “On direct products of theories,” The Journal of Symbolic Logic, vol. 17, no. 1, pp. 1–31, 1952. [3] S. Feferman and R. L. Vaught, “The first-order properties of algebraic systems,” Fundamenta Mathematicae, vol. 47, 1959. [4] J. A. 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A Categorical Account of
Composition Methods in Logic: Conclusions, Acknowledgments & References

Ever wondered how a data warehouse transforms from a concept to a cornerstone of business intelligence? 🌟 Imagine a data warehouse as a vast library, each book brimming with stories (data) waiting to be told. But how does this library come to be? It's not just about stacking books on shelves; it's an art, a science, and a journey. And who's our librarian in this tale? The ! They're the masterminds weaving together technology, strategy, and data to create a repository that not only stores information but makes it meaningful. Data Architect So, grab your hard hats and blueprint rolls! We're about to embark on the fascinating journey of building a data warehouse, guided by our adept Data Architect. Understanding the Need for a Data Warehouse A data warehouse, optimized for , is a centralized repository for structured and processed data. OLAP (Online Analytical Processing) Unlike traditional systems, it's designed for efficient querying and reporting. The use of in data warehouses allows for quicker data retrieval, especially beneficial for analytical queries. OLTP (Online Transaction Processing) columnar storage This analytical powerhouse differs from , which store raw, unstructured data, and approaches that handle large data volumes but may not optimize specific analytical queries. data lakes big data In essence, a data warehouse is not just a storage unit but a sophisticated tool for advanced analytics and reporting aiding in generating business intelligence. Dreaming and Designing Picture an architect dreaming up a grand building. That's our Data Architect, envisioning the data warehouse. It's not just a wild dream; it's a meticulously planned one, understanding the business needs, data sources, and end goals. Here, the architect might consider using a , an approach to database design that allows for intricate and multifaceted data relationships. This schema, resembling a snowflake's structure, is known for its normalization, which reduces data redundancy and improves efficiency. snowflake schema Laying the Foundations Every great building needs a solid foundation, and so does a data warehouse. This phase involves choosing the right infrastructure, which can vary significantly depending on specific needs. There are various data warehouse products available in the market, each with its unique features. On-premise data warehouses have been the traditional choice, offering control and customization. However, the rise of cloud data warehousing has brought about a shift, favoring flexibility and scalability. Cloud data warehouses like , , and offer various degrees of these services, allowing businesses to select the one that aligns best with their objectives. Amazon Redshift Google BigQuery Snowflake Most cloud providers lay out the fundamental architecture, making it easier for businesses to implement and scale their data warehouses efficiently. Our Data Architect collaborates with IT teams and , selecting the right tools and technologies. Database Engineers They're like the builders and engineers, ensuring our library is robust and weatherproof. Structuring the Warehouse Structuring the warehouse's structure is a pivotal phase. Here, the and determine the most efficient and accessible way to organize data. Often, they employ the snowflake schema, an advanced data model known for its detailed normalization. Data Architect Data Modelers In the snowflake schema, data is segmented into fact tables and dimension tables. Let’s take an example of a retail giant called 'Y', the structure could include: These tables contain quantifiable data about business events. In a retail context, this might include a 'Sales' table, capturing every sales transaction. Key metrics such as sales amount, quantity sold, and transaction timestamps would be stored here. Fact Tables: These tables provide the context for facts. For our retail company 'Y', dimension tables could include: Dimension Tables: Customer: Stores customer data like name, address, and contact information. Product: Details about products, such as product ID, name, category, and price. Store: Information on store locations, including store ID, address, and region. In the snowflake schema, dimension tables are normalized, meaning related data is further broken down into additional tables. For instance, the 'Product' might link to a 'Product Category,' providing more detailed categorization. This design allows for efficient data processing and complex querying. The separation of facts and dimensions, coupled with the detailed breakdown in the snowflake schema, supports a wide range of analytical capabilities from basic sales tracking to in-depth customer behavior analysis. Populating With Data Populating a data warehouse involves several critical steps to ensure that the data is not only accurately captured but also effectively usable for analysis. Define the Data Sources The first step involves identifying and defining all the data sources that will feed into the warehouse. This includes a variety of sources, such as internal systems like CRM and ERP, external data from market research, social media, and more. Understanding the nature, format, and quality of data from these sources is crucial for effective integration. Design the Data Model Once the data sources are identified, the next step is designing the data model. This step serves as the blueprint for how data is stored, accessed, and related within the warehouse. Configure the ETL or ELT Jobs With the data sources defined and the data model designed, the focus shifts to configuring the (Extract, Transform, Load) or (Extract, Load, Transform) jobs. ETL ELT Monitor the Data Quality The final step is the continuous monitoring of data quality after it is populated into the warehouse. This step ensures data accuracy, consistency, and integrity. Regular audits, data validation processes, and error-checking mechanisms are integral to maintaining the high quality of data in the warehouse. Various tools can assist in these steps. For ETL and ELT, popular tools like and ) can be employed. Additionally, data quality monitoring tools and software can help in maintaining the integrity of the data warehouse. Informatica PowerCenter, Talend, Apache NiFi, Microsoft SQL Server Integration Services (SSIS Testing the Integrity Before opening the doors, we need to ensure everything is in order. This is where rigorous testing comes in. Data Quality Analysts and QA Engineers scrutinize every corner of the warehouse, checking for data accuracy, performance issues, and security vulnerabilities. It's like a pre-opening inspection of our library, making sure every book is in place and the cataloging system works flawlessly. They'll sift through each test case, marking them as a pass or a fail. Any hiccups? Back to the kitchen (the development team) for tweaks. Once the final nod of approval comes from the stakeholders, it's a go-go for the final development. This testing phase is critical to ensure that the data warehouse not only meets the functional requirements but also adheres to the highest standards of data quality and security. Launching the Warehouse With a green light from testing, it's time for the grand opening. The data warehouse goes live, ready to serve its purpose. Key to this phase is establishing secure and appropriate access for different users: for general users who need to view and analyze data. Read-Only Access: for specialized roles that require modification privileges. Read-Write Access: for Data Administrators managing the warehouse. Administrative Access: Access is provisioned through Role-Based Access Control (RBAC), ensuring users have permissions tailored to their role. Strong authentication methods and regular audits of access privileges are crucial for maintaining security and efficiency as the warehouse goes live. Continuous Improvement and Maintenance A great library evolves with time, and so does a data warehouse. The Data Architect, along with the support team, monitors the warehouse, ensuring it adapts to changing business needs, data volumes, and technological advancements. They're continually optimizing, updating, and maintaining the warehouse, much like a librarian acquiring new books and preserving old ones. Wrapping It Up And there you have it, the lifecycle of a data warehouse, from a mere concept to a fully functional and evolving repository of knowledge. It's a testament to the skill, collaboration, and foresight of the Data Architect and their team, ensuring that the data warehouse not only stores data but turns it into a treasure trove of insights. So, next time you query a data warehouse, remember, it's not just a technology; it's a carefully crafted masterpiece, a library of data, built to enlighten and empower. 🚀✨

The Lifecycle of a Data Warehouse

Introduction: Large enterprises, which have more than one business unit, usually have more than one data platform environment. In fact, companies trying to be agile to make quick decisions, all have business unit-specific data platforms. With the computational advancements and rapid growth in data analytics and science, data integration tasks for data platforms are increasing exponentially as they need to support transactional as well as batch processing jobs. Here, the challenge is choosing the orchestration tool, as data integrations have to work in conjunction with different platforms. Though there are several tools in the market, in the article, we will cover how Apache airflow became the most trusted tool for the orchestration industry and its advantages. ETL: Traditional ETL (Extract, Transform, and loading) tools like Informatica, Ab Initio, and Talend all come with their own orchestration process. But as they are proprietary tools, it's expensive as well. Cloud service providers Azure, AWS (Amazon Web Services), and Google Cloud are all trying cash in the orchestration space so Azure has Synapse and AWS has Glue, Step function, etc. Basics: Orchestration is a simple or complex series of workflows defining a process. Workflows depend on one another to perform a business function or a series of business func‐tions calling other services. Then what is the ? process A process is a business function or set of business functions achieved by executing one or more tasks. Then what is the ? task A task is a step in a process. A task may call a service to perform a particular data-related function. Then what is the ? Service A service is software like Azure Synapse Analytics, Python data frame, or AWS DynamoDB that performs tasks. Common Problems in the Legacy Tools: The most common problem in the orchestration tools is their ability to successfully implement and maintain processes across the organization. Engineers and managers want to choose a tool that addresses the below issues: Dependency management: Lack of dependency management results in maintaining process-to-process and task-to-tasks dependency checks within the codebase along with business logic. This increases development time based on the number of processes or tasks involved in a workflow. Task modification to existing processes: Workflow is defined as each step within the code. Embedding the workflow in the code requires manual effort to fully review the code to make changes to one or more tasks resulting in additional time involved in project enhancements and testing. Troubleshooting: Troubleshooting a particular process should not require the need to understand the dependencies, as it results in additional support time. Restart: It should be fairly easy to restart the failed task or trigger an ad-hoc task. Orchestration should not warrant the need to rerun the entire process if a particular task within the process failed. User-friendly interface to track the progress: Orchestration tools should provide best in class user interface with the details to visualize the tasks involved within the processes in one place. Engineers investigating the issue should have a UI to understand the flow of a particular process. UI should display the current process in execution, the time it's taking, etc. Apache Airflow Airflow is a platform to programmatically author, schedule, and monitor workflows geared at data pipelining. Airflow is used to author workflows as DAGs of tasks. The Airflow scheduler executes tasks on an array of workers while following the specified dependencies. The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed. Code Development: Authoring a data pipeline using existing tasks involves writing a Python module and deploying it to the Airflow installation. Adding a new kind of task to an airflow installation involves writing and deploying Python code in packages called plugins. This aligns with the primary coding skill set of Data Engineers and scientists, which is Python. Benefits At a high level, process dependency management is easy to develop and maintain via user interface (UI) and support in Airflow, which helps immensely to save time for developers and supporting resources. DAG creation is all in Python scripting language which is the primary coding skillset for Data Engineers. Changes within Airflow can be captured using the GIT repository enabling version controls and change management practices. Airflow has good documentation and an active internal community for answering questions. What are the skillsets required to develop and maintain Airflow? Python is a major skill required to use Airflow. Initial learnings are required to set up Airflow and understand the architecture so it's evident for future maintenance. Can Engineers visualize the Jobs? Yes, Airflow has a Web interface to start and stop processes and also to monitor the current status of the processes. Does Airflow support any integration with Data Quality checkers? DataDog can be integrated with Airflow. How do we make ensure Airflow’s availability? Airflow workers and Web servers can be load balanced. Airflow Scheduler is a single point of failure. How does Airflow scale? Airflow Web server can be scaled based on the number of requests. Airflow workers can be scaled up and down based on requirements.

Apache Airflow And Its Contribution to Enterprise Data Integration

When talking about data processing, people often abbreviate it as “ETL.” However, if we look closely, data processing has undergone several iterations from ETL, ELT, XX ETL (such as Reverse ETL, Zero-ETL) to the currently popular EtLT architecture. While the Hadoop era mainly relied on ELT (Extract, Load, Transform) methods, the rise of real-time data warehouses and data lakes has rendered ELT obsolete. EtLT has emerged as the standard architecture for real-time data loading into data lakes and real-time data warehouses. Let’s explore the reasons behind the emergence of these architectures, their strengths and weaknesses, and why EtLT is gradually replacing ETL and ELT as the global mainstream data processing architecture, along with practical open-source methods. ETL Era (1990–2015) In the early days of data warehousing, Bill Inmmon, the proponent of data warehousing, defined it as a data storage architecture for partitioned subjects, where data was categorized and cleaned during storage. During this period, most data sources were structured databases (e.g., MySQL, Oracle, SQLServer, ERP, CRM), and data warehouses predominantly relied on OLTP databases (e.g., DB2, Oracle) for querying and historical storage. Handling complex ETL processes with such databases proved to be challenging. To address this, a plethora of ETL software emerged, such as Informatica, Talend, and Kettle, which greatly facilitated integrating complex data sources and offloading data warehouse workloads. Advantages: Clear technical architecture, smooth integration of complex data sources, and about 50% of the data warehouse work handled by ETL software. Disadvantages: All processing is done by data engineers, leading to longer fulfillment of business requirements; high hardware costs with double investments, especially when dealing with large data volumes. During the early and mid stages of data warehousing, when data source complexity was significantly higher, the ETL architecture became the industry standard and remained popular for over two decades. ELT Era (2005–2020) As data volumes grew, both data warehouse and ETL hardware costs escalated. New MPP (Massively Parallel Processing) and distributed technologies emerged, leading to the gradual shift from ETL to ELT architecture in the later stages of data warehousing and the rise of big data. Teradata, one of the major data warehouse vendors, and Hadoop Hive, a popular Hadoop-based data warehousing solution, adopted ELT architecture. They focused on direct loading (without complex transformation like join and group) of data into the data warehouse’s data staging layer, followed by further processing using SQL or H-SQL from the staging layer to the data atomic layer and finally to the summary layer and indicator layer. While Teradata targeted structured data and Hadoop targeted unstructured data, they adopted similar 3–4 layer data storage architectures and methodologies for data warehousing globally. Advantages: Utilizing data warehouse high-performance computing for large data volume processing, resulting in higher hardware ROI. Complex business logic can be handled by SQL, reducing the overall cost of data processing personnel by employing data analysts and business-savvy technical personnel, without the need to understand ETL tools like Spark and MapReduce. Disadvantages: Suitable for simple and large data volume scenarios. Inadequate for complex data sources and lacking support for real-time data warehousing requirements. ODS (Operational Data Store) was introduced as a transitional solution to handle complex data sources that ELT-based data warehouses couldn’t load and to improve real-time capabilities. ODS involved processing complex data sources through real-time CDC (Change Data Capture), real-time APIs, or short-batch processing (Micro-Batch) into a separate storage layer before ELT-ing them into the enterprise data warehouse. Currently, many enterprises still adopt this approach. Some companies place the Operational Data Store (ODS) within the data warehouse and use Spark and MapReduce for initial ETL (Extract, Transform, Load) processes. Later, they perform business data processing within the data warehouse (using tools like Hive, Teradata, Oracle, and DB2). At this stage, the early EtLT (Extract, Transform, Load, and Transform) community has already formed. It is characterized by a division of roles, where the complex processes of data extraction, Change Data Capture (CDC), data structuring, and standardization are often handled by data engineers, referred to as “t.” Their objective is to move data from the source system to the underlying data preparation layer or data atomic layer within the data warehouse. On the other hand, the processing of complex data atomic layers with business attributes, data aggregation, and generating data metrics (involving operations such as Group by and Join) is typically performed by business data engineers or data analysts who are skilled in using SQL. As a result of the emergence of the EtLT architecture, standalone projects like ODS have gradually faded out of the limelight due to the increase in data volume and the adoption of EtLT principles. EtLT Era (2020-Present) The EtLT architecture, as summarized by James Densmore in the , is a modern and globally popular data processing framework. EtLT emerged in response to the transformations in the modern data infrastructure. Data Pipelines Pocket Reference 2021 Background of the EtLT Architecture The modern data infrastructure has the following characteristics, which led to the emergence of the EtLT architecture: Cloud, SaaS, and Hybrid Local Data Sources Data Lake and Real-time Data Warehouses New Generation Big Data Federation Proliferation of AI Applications Fragmentation of Enterprise Data Community The appearance of complex data sources In the current global enterprise landscape, the advent of cloud and SaaS has made an already complex data source environment even more intricate. Dealing with SaaS data has given rise to a new concept of data ingestion, exemplified by tools like Fivetran and Airbyte, aiming to address the ELT (Extract, Load, Transform) challenges of ingesting SaaS data into data warehouses like Snowflake. Additionally, the complexity of data sources has increased with the proliferation of cloud-based data storage services (e.g., AWS Aurora, AWS RDS, MongoDB Service) and the coexistence of traditional on-premises databases and software (SAP, Oracle, DB2) in hybrid cloud architectures. Traditional ETL and ELT architectures are unable to cope with the intricacies of processing data in such a complex environment. Data Lake and Real-time Data Warehouses In modern data architecture, the emergence of data lakes has combined the features of traditional ODS (Operational Data Store) and data warehouses. Data lakes enable real-time data processing and facilitate data changes at the source (e.g., Apache Hudi, Apache Iceberg, Databricks Delta Lake). Simultaneously, the concept of real-time data warehouses has surfaced, with various new computing engines (e.g., Apache Pinot, ClickHouse, Apache Doris) making real-time ETL a priority. However, traditional CDC ETL tools or real-time stream processing platforms face challenges in providing adequate support for data lakes and real-time data warehouses, either due to compatibility issues with new storage engines or limitations in connecting to modern data sources, lacking robust architecture and tool support. The Emergence of a New Generation of Big Data Federation In modern data architecture, a new breed of architectures has emerged, aiming to minimize data movement across different data stores and enabling complex queries directly through connectors or rapid data loading. Examples include Starburst’s TrinoDB (formerly PrestoDB) and OneHouse based on Apache Hudi. These tools excel at data caching and on-the-fly cross-data-source queries, making them unsuitable for support by traditional ETL/ELT tools in this new Big Data Federation paradigm. The Rise of Large-scale Models With the emergence of ChatGPT in 2022, AI models have become algorithmically feasible for widespread enterprise applications. The bottleneck for AI application deployment now lies in data supply, which has been addressed by data lakes and Big Data Federation for data storage and querying. However, traditional ETL, ELT, and stream processing have become bottlenecks for data supply, either unable to quickly integrate various complex traditional and emerging data sources or failing to support diverse data requirements for both AI training and online AI applications using a single codebase. Fragmentation of the Enterprise Data Community As data-driven approaches become more ingrained in enterprises, the number of data users within organizations has rapidly increased. These users range from traditional data engineers to data analysts, AI practitioners, sales analysts, and financial analysts, each with diverse data requirements. After experiencing various shifts like No-SQL and New-SQL, SQL has emerged as the sole standard for complex business analysis. A considerable number of analysts and business unit engineers now use SQL to address the “last mile” problem of data analysis within enterprises. Meanwhile, the handling of complex unstructured data is left to professional data engineers using technologies like Spark, MapReduce, and Flink. Consequently, the demands of these two groups diverge significantly, making traditional ETL and ELT architectures inadequate to meet the needs of modern enterprise users. EtLT Architecture Emerges! Against the backdrop mentioned above, data processing has gradually evolved into the EtLT architecture: EtLT Architecture Overview: EtLT splits the traditional ETL and ELT structures and combines real-time and batch processing to accommodate real-time data warehouses and AI application requirements. E(xtract) — Extraction: EtLT supports traditional on-premises databases, files, and software as well as emerging cloud databases, SaaS software APIs, and serverless data sources. It can perform real-time CDC on database binlog and real-time stream processing (e.g., Kafka Streaming) and also handle bulk data reading (multi-threaded partition reading, rate limiting, etc.). t(ransform) — Normalization: In addition to ETL and ELT, EtLT introduces a small “t,” focusing on data normalization. It rapidly converts complex and heterogeneous extracted data sources into structured data that can be readily loaded into the target data storage. It deals with real-time CDC by splitting, filtering, and changing field formats, supporting both batch and real-time distribution to the final Load stage. L(oad) — Loading: The loading stage is no longer just about data loading but also involves adapting data source structures and content to suit the target data destination (Data Target). It should handle data structure changes (Schema Evolution) in the source and support efficient loading methods such as Bulk Load, SaaS loading (Reverse ETL), and JDBC loading. EtLT ensures support for real-time data and data structure changes, along with fast batch data loading. (T)ransform — Conversion: In cloud data warehouses, on-premises data warehouses, or new data federations, business logic is processed. This is typically achieved using SQL, either in real-time or batch mode, to transform complex business rules accurately and quickly into data usable by business or AI applications. In the EtLT architecture, different user roles have distinct responsibilities: EtL Phase: Primarily handled by data engineers who convert complex and heterogeneous data sources into data that can be loaded into the data warehouse or data federation. They do not need to have an in-depth understanding of enterprise metric calculation rules but must be proficient in transforming various source and unstructured data into structured data. Their focus is on data timeliness and the accuracy of transforming source data into structured data. T Phase: Led by data analysts, business SQL developers, and AI engineers who possess a deep understanding of enterprise business rules. They convert business rules into SQL statements to perform analysis and statistics on the underlying structured data, ultimately achieving data analysis within the enterprise and enabling AI applications. Their focus is on data logic relationships, data quality, and meeting business requirements for final data results. Open-Source Implementation of EtLT There are several open-source implementations of EtLT in modern data architecture. Examples include DBT, which helps analysts and business developers quickly develop data applications based on Snowflake, and Apache DolphinScheduler, a visual workflow orchestration tool for big data tasks. DolphinScheduler plans to introduce a Task IDE, allowing data analysts to directly debug SQL tasks for Hudi, Hive, Presto, ClickHouse, and more, and create workflow tasks through drag-and-drop. As a representative of the EtLT architecture, Apache SeaTunnel started with support for various cloud and on-premises data sources, gradually expanding its capabilities to include SaaS and Reverse ETL, as well as accommodating the demands of large-scale model data supply. It has been continually refining the EtLT landscape. The latest SeaTunnel Zeta computing engine delegates complex operations such as Join and Groupby to the ultimate data warehouse endpoint, focusing on data normalization and standardization. This approach aims to achieve the goal of unified real-time and batch data processing with a single set of code and a high-performance engine. Additionally, SeaTunnel now includes support for large-scale models, making it possible for these models to directly interact with over 100 supported data sources (refer to ), ranging from traditional databases to cloud databases and ultimately SaaS. https://seatunnel.apache.org/docs/Connector-v2-release-state/ Since joining the Apache Incubator in late 2022, Apache SeaTunnel has witnessed a five-fold growth in one year, and currently, it supports more than 100 data sources (as seen at ). The connector support has been progressively improved, encompassing traditional databases, cloud databases, and SaaS offerings. https://seatunnel.apache.org/docs/2.3.2/category/source-v2 The release of SeaTunnel Zeta Engine in Apache SeaTunnel 2.3.0 brings features such as data distributed CDC, schema evolution for target source data tables, and the synchronization of entire databases and multiple tables. Its excellent performance has garnered attention from numerous global users, including Bharti Airtel, the second-largest telecommunications operator in India, Shopee.com, an e-commerce platform in Singapore, and Vip.com, a major online retailer. Large Scale Model Training support One noteworthy aspect is that SeaTunnel now offers support for large-scale model training and vector databases, enabling seamless interactions between large models and the 100+ supported data sources of SeaTunnel (see the article ). Furthermore, SeaTunnel can leverage ChatGPT to directly generate SaaS Connectors, facilitating rapid access to a wide range of internet information for your large-scale models and data warehouses. Breakthrough in the book search field! Use Apache SeaTunnel, Milvus, and OpenAI to improve the accuracy and efficiency of book title similarity search As the complexity of AI, cloud, and SaaS continues to increase, the demand for real-time CDC, SaaS, data lakes, and real-time data warehouse loading has made simple ETL architectures inadequate to meet the needs of modern enterprises. EtLT architecture, tailored for different stages of enterprise development, is destined to shine in the modern data infrastructure. With the mission of “Connecting the world’s data sources and synchronizing them as swiftly as flying”, Apache SeaTunnel warrants the attention of all data professionals. Also published . here

ELT is Dead, and EtLT Will End Modern Data Processing Architecture

Fines related to sensitive data exposure are growing. For instance, major GDPR violations can cost companies up to , while gross HIPAA violations can result in imprisonment. 4% of their annual global turnover Your production environment might be thoroughly protected. But what is about testing initiatives and sales demos? Are you confident in the third-party contractors that have access to your sensitive data? Will they do their best to protect it? To ensure compliance and data safety, companies are turning to . If you are also interested, check out this guide answering the three important questions: data management service providers What is data masking? Why and when do you need it, and How could your company implement it successfully? It also presents a detailed data masking example from our portfolio. After reading the article, you will have enough information to negotiate with data masking vendors. Understanding data masking So, what is data masking? Data masking is defined as building a realistic and structurally similar but nonetheless fake version of organizational data. It alters the original data values using manipulation techniques while maintaining the same format and delivers a new version that can’t be reverse-engineered or tracked back to the authentic values. Here is an example of masked data: Do you need to apply data masking algorithms to all the data stored within your company? Most likely not. Here are the data types that you definitely need to protect: includes medical records, lab tests, medical insurance information, and even demographics. Protected Health Information (PHI) is related to credit and debit card information and transaction data under the Payment Card Industry Data Security Standard (PCI DSS). Payment card information , such as passport and social security numbers. Basically, any piece of information that can be used to identify a person. Personally identifiable information (PII) includes inventions, such as designs, or anything that has value to the organization and can be stolen. Intellectual property (IP) Why do you need data masking? Data masking protects sensitive information utilized for non-productive purposes. So, as long as you use any of the sensitive data types presented in the previous section in training, testing, sales demos, or any other types of non-production activities, you need to apply data masking techniques. This makes sense as non-production environments are normally less protected and introduce . more security vulnerabilities Moreover, if there is a need to share your data with third-party vendors and partners, you can grant access to masked data instead of forcing the other party to comply with your extensive security measures to access the original database. Statistics show that take place due to compromises on the business partner’s side. 19% of data breaches Additionally, data masking can provide the following advantages: Renders organizational data useless to cybercriminals in case they are able to access it Reduces risks posed by sharing data with authorized users and outsourcing projects Helps comply with data privacy and security-related regulations, such as the General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), and any other regulations applicable within your field Protects data in the case of deletion, as the conventional file deletion methods still leave a trace of the old data values Safeguards your data in the case of unauthorized data transfer Data masking types There are five main types of data masking that aim to cover different organizational needs. 1. Static data masking Implies creating a backup of the original data and keeping it safe in a separate environment for production use cases. Then it disguises the copy by including fake but realistic values, and makes it available for non-production purposes (e.g., testing, research), as well as sharing with contractors. 2. Dynamic data masking Aims to modify an excerpt of the original data at runtime when receiving a query to the database. So, a user who is not authorized to view sensitive information queries the production database, and the response is masked on the fly without changing the original values. You can implement it via database proxy, as presented below. This data masking type is normally used in read-only settings to prevent overriding production data. 3. On-the-fly data masking This data masking type disguises data when transferring it from one environment to another, such as from production to testing. It is popular with organizations that continuously deploy software and perform large data integrations. 4. Deterministic data masking Replaces column data with the same fixed value. For instance, if you want to replace “Olivia” with “Emma”, you have to do it in all the associated tables, not only in the table you are currently masking. 5. Statistical data obfuscation This is used to reveal information about patterns and trends in a dataset without sharing any details on actual people represented there. 7 main data masking techniques Below you can find seven of the most popular data masking techniques. You can combine them to cover the various needs of your business. . You can shuffle and reassign data values within the same table. For example, if you shuffle the employee name column, you will get the real personal details of one employee matched to another. Shuffling . Rearranges characters and integers of a data field in random order. If an employee’s original ID is 97489376, after applying shuffling, you will receive something like 37798649. This is restricted to specific data types. Scrambling . This is a simple masking strategy where a data field is assigned a null value. This method has limited usage as it tends to fail the application’s logic. Nulling out . Original data is substituted by fake but realistic values. Meaning that the new value still needs to satisfy all domain constraints. For instance, you substitute someone’s credit card number with another number that conforms to the rules enforced by the issuing bank. Substitution . This is mostly applicable to financial information. One example is masking original salaries by applying +/-20% variance. Number variance . This method increases or decreases a date by a specific range, maintaining that the resulting date satisfies the application’s constraints. For instance, you can age all contracts by 50 days. Date aging . Involves replacing all the original data values by an average. For instance, you can replace every individual salary field by an average of salary values in this table. Averaging How to implement data masking the right way? Here is your 5-step data masking implementation plan. Step 1: Determine the scope of your project Before you start, you will need to identify which aspects you will cover. Here is a list of typical questions that your data team can study before proceeding with the masking initiatives: Which data are we looking to mask? Where does it reside? Who is authorized to access it? What is the access level of each user from the above? Who can only view and who can alter and delete values? Which applications are utilizing this sensitive data? What impact will data masking have on different users? What level of masking is required, and how often will we need to repeat the process? Are we looking to apply data masking across the whole organization or limit it to a specific product? Step 2: Define the stack of data masking techniques During this step, you need to identify which technique or a combination of data masking tools are the best fit for the task at hand. First of all, you need to identify which data types you need to mask, for instance, names, dates, financial data, etc., as different types require dedicated data masking algorithms. Based on that, you and your vendor can choose which open-source library(s) can be reused to produce the best-suited data masking solution. We advise turning to a , as they will help you customize the solution and integrate it painlessly into your workflows across the whole company without interrupting any business processes. Also, it’s possible to build something from zero to cover the company’s unique needs. software vendor There are ready-made data masking tools that you can purchase and deploy yourself, such as , , , and many more. You can opt for this strategy if you manage all your data by yourself, you understand how different data flows work, and you have an IT department that can help integrate this new data masking solution into the existing processes without hindering productivity. Oracle Data Masking IRI FieldShield DATPROF Step 3: Secure your selected data masking algorithms The security of your sensitive data largely depends on the security of the selected fake data-generating algorithms. Therefore, only authorized personnel can know which data masking algorithms are deployed, as these people can reverse engineer the masked data to the original dataset with this knowledge. It’s a good practice to apply separation of duties. For instance, the security department selects the best-suited algorithms and tools, while data owners maintain the settings applied in masking their data. Step 4: Preserve referential integrity Referential integrity means that each data type within your organization is masked in the same way. This can be a challenge if your organization is rather large and has several business functions and product lines. In this case, your company is likely to use different data masking algorithms for various tasks. To overcome this issue, identify all the tables that contain referential constraints and determine in which order you will mask the data as parent tables should be masked before the corresponding child tables. After completing the masking process, do not forget to check whether referential integrity was maintained. Step 5: Make the masking process repeatable Any adjustment to a particular project, or just general changes within your organization, can result in modifying the sensitive data and creating new data sources, posing the need to repeat the masking process. There are instances where data masking can be a one-time effort, such as in the case of preparing a specialized training dataset that will be used for a few months for a small project. But if you want a solution that will serve you for a prolonged time, your data can become obsolete at one point. So, invest time and effort in formalizing the masking process to make it fast, repeatable, and as automated as possible. Develop a set of masking rules, such as which data has to be masked. Identify any exceptions or special cases that you can foresee at this point. Acquire/build scripts and to apply these masking rules in a consistent manner. automated tools Your checklist for selecting a data masking solution Whether you work with a software vendor of your choice or opt for a ready-made solution, the final product needs to follow these data-masking best practices: Be non-reversible, making it impossible to reverse engineer the fake data to its authentic values Protect the integrity of the original database and doesn’t render it useless by making permanent changes by mistake Mask non-sensitive data if this is necessary to protect sensitive information Provide an opportunity for automation, as the data will change at some point and you don’t want to start from zero every time Generate realistic data that maintains the structure and the distribution of the original data, and satisfies business constraints Be scalable to accommodate any additional data sources that you want to incorporate into your business Compliant with all the applicable regulations, such as HIPAA and GDPR, and your internal policies Integrate well into the existing systems and workflows Data masking challenges Here is a list of challenges that you might face during implementation. Format preservation. The masking solution has to understand the data and be able to preserve its original format. Gender preservation. The selected data masking methodology needs to be aware of gender when masking people’s names. Otherwise, the gender distribution within the dataset will be altered. Semantic integrity. The generated fake values need to follow the business rules restricting different data types. For instance, salaries have to fall within a specific range, and social security numbers have to follow a predetermined format. This is also true for maintaining the geographical distribution of the data. Data uniqueness. If the original data has to be unique, like an employee ID number, the data masking technique needs to supply a unique value. Balancing security and usability. If the data is too heavily masked, it can become useless. On the other hand, if it’s not protected enough, users can gain unauthorized access. Integrating the data into the existing workflows might be highly inconvenient to employees in the very beginning, as people are used to working in a certain way, which is currently being disrupted. A data masking example from the ITRex portfolio An international healthcare organization was looking to obscure sensitive personally identifiable information (PII) presented in multiple formats and residing in both production and non-production environments. They wanted to build an that can discover and obfuscate PII while complying with the company’s internal policies, GDPR, and other data privacy regulations. ML-powered data masking software Our team immediately noticed the following challenges: The client had enormous volumes of data, over 10,000 data sources, and many corresponding data flows There was no clear data masking strategy that would cover all the different departments Due to this large variety, our team wanted to come up with a set of policies and processes that would guide different dataset owners on how to mask their data and would serve as the basis for our solution. For instance, someone could come up with a list of data points that they want to obfuscate, whether once or continuously, and the solution, guided by these principles, would study the data and select appropriate obfuscation techniques and apply them. We approached this project by surveying the landscape through the following questions: Which data management solutions are you using? The client was already using Informatica, so we went with that. Informatica’s data masking solution offers out-of-the-box features, which satisfied some of the client’s needs, but this wasn’t enough to cover all the requirements. Which data types are you willing to mask? Due to a large number of data sources, it was impossible to address everything at once. So, we asked the client to prioritize and identify what was mission-critical. Do you want to do it one time, or make it a repeatable process? After answering these questions, we suggested providing data masking as a service mainly because the client has too many data sources, to begin with, and it might have taken years to cover them all. In the end, we delivered data masking services with the help of a custom ML-driven tool that can semi-automatically perform data masking in four steps: Identify data types. Data owners put their data sources into the analysis tool that studies the columns’ data and reveals the data types it could identify in these columns, such as addresses, phone numbers, etc. A human expert verifies its output, allowing it to learn from mistakes. Suggest masking approaches for each column and apply them after human approval Deploy the results. After the masked data is generated, it needs to be deployed. We provided multiple options for data storage. This includes, but is not limited to, using a temporary database that remains live for several days, assigning a permanent location for masked environments, generating comma-separated values (CSVs) files, and more. Examine and give a badge of approval to a set of data or a set of environments as proof that they are properly masked and compliant This data masking solution helped the client comply with GDPR, dramatically reduced the time needed to form non-production environments, and lowered the costs of transferring data from production to sandbox. How to maintain masked data after implementation? Your efforts do not stop when confidential data is masked. You still need to maintain it over time. Here are the steps that will help you in this initiative: Establish policies and procedures that govern the masked data. This includes determining who is authorized to access this data and under which circumstances and which purposes this data serves (e.g., testing, reporting, research, etc.) Train employees on how to use and protect this data Regularly audit and update the masking process to ensure it remains relevant Monitor masked data for any suspicious activities, such as unauthorized access attempts and breaches Perform masked data backups to make sure it’s recoverable Concluding thoughts Data masking will protect your data in non-production environments, enable you to share information with third-party contractors, and help you with compliance. You can purchase and deploy a data obfuscation solution yourself if you have an IT department and control your data flows. However, keep in mind that improper data masking implementation can lead to rather unpleasant consequences. Here are some of the most prominent ones: Hindering productivity. The selected data masking techniques can cause large unnecessary delays in data processing, thereby slowing employees. Becoming vulnerable to data breaches. If your data masking methods, or the lack thereof, fail to protect sensitive data, there will be financial and legal consequences up to serving time in prison. Deriving inaccurate results from data analysis. This can happen if the data is masked incorrectly or too heavily. Researchers will misinterpret the experimental dataset and reach faulty conclusions that will lead to unfortunate business decisions. Hence, if a company isn’t confident in its abilities to execute data obfuscation initiatives, it’s best to contact an external vendor who will help select the right data masking techniques and integrate the final product into your workflows with minimal interruptions. Stay protected! Considering to implement a data masking solution? ! We will help you prioritize your data, build a compliant obfuscation tool, and deploy it without interrupting your business processes. Get in touch

Data Masking: How it Can be Implemented Correctly

Enmascaramiento de datos: cómo se puede implementar correctamente

Masquage des données : comment l'implémenter correctement

डेटा मास्किंग: इसे सही तरीके से कैसे लागू किया जा सकता है

データ マスキング: 正しく実装する方法

Mascaramento de dados: como pode ser implementado corretamente

Mặt nạ dữ liệu: Làm thế nào nó có thể được thực hiện một cách chính xác

数据屏蔽：如何正确实施

Whether it is answering a question or Elon preaching self-driving cars, business leaders see exciting machine learning use cases everywhere they look. The hype around artificial intelligence (AI) and machine learning (ML) is staggering. So much of the mainstream discussion centers around these exciting applications, but they often leave out the inputs and processes required to build these systems. Alexa Quick digression (Optional). Most media will incorrectly lump all AI applications as the same type. However, there are two types of artificial intelligence: narrow and general. Narrow AI optimizes a specific task or objective, which is 99.99% of use cases out there. Outside of this narrow objective, a human is smarter in every other situation. General AI is developing intelligence that is broader and more human-like (e.g. the Terminator). There is a HUGE difference between the AI used to forecast the weather and the AI to build the Terminator. What is Required to Build a Machine Learning System? The machine learning development lifecycle requires 4 key components: Business Problem Data Machine Learning Algorithm IT Infrastructure Stakeholders requesting a machine learning system define the business problem that is in need of a solution (e.g. forecasting future sales). A tech professional will create a model by training a machine learning algorithm based on a relevant dataset. The trained model will output a prediction based on the patterns found in the dataset. Underpinning this model is the IT infrastructure (e.g. AWS). The infrastructure is required for the storage, compute, and deployment involved in the development lifecycle. Data requires storage. Data prep and model training requires compute (i.e. CPU/GPU). Model deployment and serving requires a host (e.g. API endpoint in the cloud). What is Machine Learning? A simple example of machine learning would be predicting what I wear based on the weather. If you knew the temperature outside, the wind chill, humidity, and what I wore over the past 2 years, then you could likely create a model. The weather indicators (e.g. temp, humidity, wind) would be used to predict what I wear each day. There are likely patterns that map between the weather outside and what I chose to wear that day. Train the model on this data and voila — you have your predictive model. How could this possibly go wrong? Note: The weather indicators are referred to as features, predictors, or input. “What I wear each day” is referred to as the target variable or output. Why do ML Systems Fail? Many stakeholders have the misconception that machine learning is magic. All you need is some data fed into an algorithm and the result is a beautiful model. This couldn’t be further from the truth. Machine learning is a process that finds patterns in the data between the input (features/predictors) and output (target variable). Mathematical functions are used to map each type of input to the most common output in that scenario. The “magic” requires stable, unique patterns between various combinations of the input and output. If the patterns are unstable, or fail to provide enough signal, then the model will likely be useless. This branch of data science is called, data quality. Data quality refers to the set of activities applying quality management techniques to data in order to ensure it is fit to serve the needs of an organization in a specific context or problem. Data quality is the root cause for why many machine learning systems fail. The primary for measuring data quality are the following: dimensions : The data reflects the real-world objects and/or events it is intended to model Accuracy : The data makes all required records and values available Completeness : Data values drawn from multiple locations do not conflict Consistency : The data conforms to defined business rules and falls within allowable parameters when those rules are applied Validity : No record exists more than once in the dataset, even if it exists in multiple locations. Every record can be uniquely identified and accessed within the dataset and across applications. Uniqueness : Data is updated as frequently as necessary to ensure it meets user requirements for accuracy, accessibility, and availability Timeliness While specifies these 6 dimensions and their respective definitions, I would add the following: Informatica of information to encourage uniqueness of records Diversity sample of data from the population to reduce biases in how data was captured and what data was captured Representative data ensures that each value that is incorrect, corrupted, incorrectly formatted, duplicate, or incomplete is fixed or removed Clean Note: If you want to read about other dimensions, I’d check out this from Aug, 1991. MIT paper How Does Poor Data Quality Cause Failure? The weather model would be useless, or worse*, if the data doesn’t meet the standards of high quality per each of the dimensions listed above. Using the table below, we can zoom into the data to see how poor data quality could present itself. Assume that this information represents one adult, the weather where they live and work, and the clothes they wore that day. Data Quality Issue #1: Unclean Data Data scientists and data engineers spend the majority of their time cleaning data. The lack of standardized formatting in the “Humidity” column is problematic. Without standardized formatting, data is unclear. For example, is the “.05” humidity from January 3rd the actual humidity, a typo, or did the user intend to convert .05 to 5 percent? Best practice would suggest only storing numeric data in this column and specifying the “percentage” label in the column header or another document. If I received this dataset, I would remove all “%” signs, any “perc” or “percent” text, remove any whitespace, and either convert the Jan 3 humidity value from .05 to 5 or remove it completely. Data Quality Issue #2: Lack of Diverse data Ignoring the formatting issues, do you notice anything strange about this dataset? Why would someone wear a short sleeve shirt and shorts in January when it is freezing outside? Take a moment and try to brainstorm possible explanations before you continue reading. If you are struggling with ideas, I would suggest questioning each assumption you made about the dataset. . . . . . Here are 5 possible explanations I could come with for why someone would wear shorts and a t-shirt during a cold day in January: Temperature was recorded incorrectly by the user. The clothing worn was recorded incorrectly by the user. The user recorded the temperature in Celsius when it should be Fahrenheit. 22 degrees Celsius converts to 71.6 degrees Fahrenheit. The person never went outside and had to endure the cold temperatures. The person only went outside to exercise. While you are running, your body temperature increases. After digging into this problem further, imagine that we learned this person works from home some days. The dataset above is excluding this important piece of information. Without knowing whether the person is staying home or going to work, the dataset is likely not informative enough. Take a look at the temperature and clothes worn each day. The person seems to wear a short sleeve shirt and shorts on most days, regardless of the temperature. If you feed a model this data, it will likely predict a short sleeve shirt and shorts in the Winter and Summer because of the lack of differentiation across records. Data Quality Issue #3: Non-representative Data Set A dataset must take a sufficient sample of the entire population of data that aims to reduce bias. In the table above, there is no information captured in February, July, August, September, November, and December. There could be important information about how this person dresses during these months that is different than the other months. If this person is a teacher, they might have July and August off and dress in a swim suit because they go to the beach those months. The omission of these months adds bias to the model and limits its accuracy. Other Data Quality Issues While I chose to focus on these 3 data quality issues, the dataset had many more. Below is a list of other data quality problems, or problems, present within the dataset. potential Each column should only have one piece of information (e.g. month rather than full date) The year that the date represents should be captured The dataset is not a sufficient size to create a decent model It is unclear what time of day any weather columns represent It is unclear what time of day the clothing worn represents Column labels should not be included in the values, but rather in column headers or in another document (e.g. data definition document) Categorical data needs to be converted to numeric data before model training How Often Do These Situations Occur? Data quality issues are something you need to consider when analyzing data or making data-driven decisions. In a recent project at work, I had to explain to stakeholders that the machine learning model I developed cannot improve without changes to the data. There was not enough diversity of information for the patterns to yield a higher level of accuracy. always Because our society is becoming increasingly data-driven, data quality will continue to be a crucial branch of data science. Before you kick off development for the next data-intensive application, I suggest checking the data’s quality or seeking the opinion of data professionals who work with it every day. Also published by The Data Generalist here Data Science Career Advisor A model that draws the wrong conclusions is worse than a clearly useless model that gets ignored. Sometimes developing a proof of concept is necessary to convince stakeholders that a larger effort must be undertaken to improve data quality. This situation is one reason why iterative improvement (e.g. agile) is often touted as the best practice in software development. Image source: Stable Diffusion AI Demo

Poor Data Quality is the Bane of Machine Learning Models

Python and JavaScript are two of the most used programming languages. Even though both are object-oriented programming languages, they have different scopes. Knowing what Python and JavaScript have in common and how they vary is crucial in the Python vs. JavaScript argument. Even though they have many similarities, there are also some significant distinctions that you should be aware of before you start studying either one. Let's first go through a broad overview of the two languages before breaking down the main distinctions between JavaScript and Python. Python At Centrum Wiskunde & Informatica, Guido Van Rossum invented Python in 1980. (CWI). encourages programmers to reuse their code by supporting the modules and packages. Python's strength and adaptability have made it an indispensable tool in almost every scientific application worldwide. It is a multipurpose programming language that is compatible with several paradigms. It is extensively utilized in various scientific and specialized fields, including astronomy, data science, artificial intelligence, machine learning, computer science education, computer vision, and image processing. Web development is another common use case for it. Python is used in back-end development, the part of web design responsible for producing aspects that consumers don't see, such as an application's server side. Python Features Some of the top features of Python are Highly portable Object-oriented Interpreted language Embedded Integrated Run-time memory allocation GUI support Advantages Python is a prevalent language that is preferred by most developers and clients too. Here are some of the top advantages of Python, Completely easy to learn and understand Extensive and huge libraries High productivity Interpreted language Open-source Highly supportive community Flexibility Companies that use Python Many huge businesses and giants have embraced Python for developing a perfect solution. Here are some of the big names using Python, Facebook Google Instagram Netflix Quora Spotify Dropbox Reddit JavaScript JavaScript, sometimes referred to as JS, is an object-oriented, lightweight, just-in-time compiled computer language used to create web pages and online applications. In September 1995, Netscape debuted JavaScript under the moniker LiveScript. It changed its name to Javascript later that year. It was first utilized for online pages and browsers. It is now present in several contexts and applications. High-level programming language JavaScript complies with ECMAScript requirements. 97 percent of websites, according to Wikipedia, employ JavaScript on the client side to improve user experience. Features The top features that JavaScript offers are, Platform independent Input validations Browser auto-detection Client-side calculations Property shorthand Perfect control Advantages JavaScript development services are considered one of the most adaptable and highly versatile languages, let’s see more reasons to choose JavaScript that make it the developer’s first choice. Highly rich and interactive interface Simple to understand Easy to learn High speed Reduced server load Huge community Versatility Offers extended functionalities Companies that use JavaScript The major tech giants that use JavaScript for their businesses have praised it a lot. Let’s have a look at the major ones, PayPal Microsoft Uber Groupon Walmart eBay Google LinkedIn Differences: JavaScript vs. Python Parameter JavaScript Python Data types No concept of different data types Mutable(set) and immutable(list) data types Numeric types Floating-point number Int, fixed-point, decimal, etc. Encoding UTF-16 ASCII Model Class-based inheritance model Prototype-based inheritance Code blocks Curly brackets Indentation Modules Limited modules including Date, JSON, math, etc. Wide range of modules. Attributes JavaScript exhibits properties that can be composed of underlying attributes With descriptor protocol, and getter setter functions, Python allows defining attributes Functional arguments JavaScript gives an undefined value to any missing parameter in case of a syntax error or a different parameter In case of any incorrect parameter in a function, Python instantly raises an exception Python or JavaScript - When to choose what? Now, you might be wondering about which languages to choose in which scenarios. Well, here is a brief description of when to choose what, Python development or Java development. When to use Python? For individuals who wish to integrate sophisticated features and modules into their applications, Python is a suitable option. Without a doubt, Python can be used for difficult mathematical computations, artificial intelligence, machine learning, data visualization, and game creation. Python may be used to create complex data-handling programs. Additionally, Python's built-in libraries make it simple to implement most functionality. Python is often advised for server-side use. When to use JavaScript? For creating websites and mobile apps, is a superior option. This is due to the fact that it quickly and immediately runs on the browser. Because it functions well with HTML and CSS, it is also a superior option for web development. JavaScript is the best option if you want to maximize memory or performance. In general, client-side usage is advised. JavaScript framework Conclusion Both Python and JavaScript are robust programming languages with various practical uses. Python has a wide range of applications and is utilized in science. On the other hand, online and mobile apps employ JavaScript. Both languages offer unique features, use cases, benefits, and drawbacks. Although there are some distinctions between the two languages, you can accomplish practically everything with either one. We hope that by now you are aware of the differences between JavaScript and Python and can make an informed decision based on your needs. Because strong programs may be written in both languages. FAQs Which is better, Python or JavaScript? Because JavaScript runs in the browser and Python is a server-side backend language, JavaScript is unquestionably superior to Python for creating websites. Python can help build websites, but it can't do it all by itself. Can I replace JavaScript with Python for web development? Python cannot fully replace JavaScript since Python is not a front-end or browser-native language. JavaScript and Python are not native to the web (BACK-END). Will JavaScript take over Python? No, both JavaScript and Python have features to offer that match different scenarios. So, in some cases, Python exhibits more advantages, while JavaScript takes over. Which is more versatile, JavaScript or Python? Both Python and JavaScript have a wide range of applications, although JavaScript is more adaptable. Everything uses JavaScript, even script tags on your website. It is in charge of the user interface, but it may also function on the server side using the Node. js framework.

Python vs. JavaScript: A Comparative Review 

As the world becomes increasingly digital, nearly every company is looking for to code—no matter the industry. The phenomenon has been great news for software engineers and programmers who can now command high salaries for their hot, in-demand talent. software engineers Perhaps it is no surprise that software engineers in the San Francisco Bay Area, home to Silicon Valley and its many tech giants, can command an even higher premium with their pay packages. According to the more than 5 million verified professionals on the professional social network Blind, these are the top 17 best-paying cities for software engineers in the San Francisco Bay Area. Blind considered the annual base pay and the total compensation package, including the annual salary and any stock-based compensation or bonuses a company might offer. 1. Los Gatos, California n: $500,449 Average Total Compensatio : $475,000 Median Total Compensation : $470,015 Average Salary : $451,000 Median Salary Los Gatos is the best-paying city for software engineers in the U.S., according to Blind. The small town is home to Netflix, which prefers to hire more senior or experienced professionals and pays accordingly. The video-streaming giant typically pays large salaries and gives few employees stock-based compensation. 2. Menlo Park, California : $328,423 Average Total Compensation : $297,000 Median Total Compensation : $167,004 Average Salary : $165,000 Median Salary Menlo Park might be home to only 34,000 people, but its population can swell in size with tech workers during the workweek. Menlo Park is home to the headquarters of Meta, the owner of Facebook and one of the highest-paying technology companies. Google and Roblox were also founded in the city. 3. Cupertino, California n: $279,391 Average Total Compensatio : $255,850 Median Total Compensation : $165,753 Average Salary : $165,000 Median Salary Cupertino is home to Apple and its futuristic Apple Park corporate campus. Software engineers at Apple have enjoyed large paychecks recently, as the iPhone maker briefly topped a jaw-dropping $3 trillion market cap early in 2022. 4. San Bruno, California : $278,368 Average Total Compensation : $225,900 Median Total Compensation : $161,269 Average Salary : $149,500 Median Salary YouTube moved its headquarters to San Bruno in 2007 and has become the city’s largest employer. The Google-owned video streaming service employs more than 2,000 professionals. 5. Mountain View, California : $275,181 Average Total Compensation : $242,000 Median Total Compensation : $160,790 Average Salary : $156,000 Median Salary Mountain View is synonymous with Silicon Valley. Long before startups were birthed in San Francisco Bay Area garages, the city was home to silicon semiconductor producers, which gave the region its name. Of course, the city is now well-known as the headquarters of Google, but other tech companies with prominent presences include 23andMe, Intuit, Mozilla and Synopsys. 6. Oakland, California : $266,869 Average Total Compensation : $197,750 Median Total Compensation : $192,240 Average Salary : $168,750 Median Salary Oakland has become a hub for many financial-technology companies. Credit Karma moved its headquarters from San Francisco to downtown Oakland in 2020. Block and Marqeta also have major offices in “The Town.” 7. San Francisco, California : $265,050 Average Total Compensation : $225,000 Median Total Compensation : $165,042 Average Salary : $160,200 Median Salary San Francisco might be the crown jewel of the technology industry, as the home of too many tech startups and companies to count. The city by the bay is home to Airbnb, DoorDash, Lyft, Twitter, Uber and more, but its top employer is . The cloud giant employs more than 9,000 employees, and its headquarters has made a permanent contribution to the downtown skyline. Salesforce 8. Sunnyvale, California : $260,048 Average Total Compensation : $238,000 Median Total Compensation : $160,499 Average Salary : $160,000 Median Salary Sunnyvale maintains its Silicon Valley roots as home to the chip-maker AMD and advanced technology and networking companies, including Juniper Networks, Microsoft-owned LinkedIn and NetApp. 9. Palo Alto, California : $234,739 Average Total Compensation : $200,000 Median Total Compensation : $157,488 Average Salary : $154,000 Median Salary The tony city of Palo Alto is one of the major cities of Silicon Valley. Cloudera and VMware are some of the largest technology companies based in the city. 10. San Mateo, California : $234,066 Average Total Compensation : $187,875 Median Total Compensation : $158,629 Average Salary : $155,500 Median Salary Companies that call San Mateo home include Coupa, Roblox and Snowflake. Before being acquired by Zendesk, Momentive, which was founded as SurveyMonkey, was headquartered in the city. 11. Pleasanton, California : $225,058 Average Total Compensation : $202,000 Median Total Compensation : $150,887 Average Salary : $150,000 Median Salary Pleasanton is home to many business parks, but the suburb’s top employer is the HR-technology giant Workday. 12. Foster City, California : $220,293 Average Total Compensation : $190,000 Median Total Compensation : $158,319 Average Salary : $148,500 Median Salary Foster City is the headquarters site of the biopharmaceutical giant Gilead Sciences and the enterprise software company Zuora, among other companies. 13. Fremont, California : $218,913 Average Total Compensation : $180,000 Median Total Compensation : $144,638 Average Salary : $142,500 Median Salary Notable companies with headquarters in Fremont include Corsair, Synnex and more, although the city’s No. 1 employer is Tesla. 14. Santa Clara, California : $214,168 Average Total Compensation : $197,000 Median Total Compensation : $153,885 Average Salary : $150,000 Median Salary AMD, Intel, NVIDIA and Palo Alto Networks are some of the most well-known tech companies headquartered in Santa Clara. 15. San Jose, California n: $210,801 Average Total Compensatio : $183,000 Median Total Compensation : $153,390 Average Salary : $145,000 Median Salary The largest city by population in the San Francisco Bay Area, San Jose is home to Adobe, Cisco, eBay, PayPal, Zoom and more. 16. Redwood City, California : $207,955 Average Total Compensation : $190,000 Median Total Compensation : $156,248 Average Salary : $151,000 Median Salary Redwood City is the headquarters location for several global technology companies, such as Box, Carbon, Electronic Arts and Informatica. 17. Milpitas, California : $188,146 Average Total Compensation : $165,750 Median Total Compensation : $154,073 Average Salary : $138,000 Median Salary Some of the top employers in Milpitas are tech employers, including Cisco, FireEye, SanDisk and more. Methodology Blind analyzed the base salaries and total compensation of software engineers in the San Francisco Bay Area on its platform. Cities were ranked by the average total compensation, including annual base salary and, if applicable, stock-based compensation and cash bonuses. All data is self-reported by verified professionals on the professional social network Blind. Also published on Teamblind's blog

17 Highest-Paying Cities in the San Francisco Bay Area for Software Engineers

Las 17 ciudades que mejor pagan en el área de la bahía de San Francisco para ingenieros de software

17 villes les mieux rémunérées de la région de la baie de San Francisco pour les ingénieurs logiciels

सॉफ्टवेयर इंजीनियरों के लिए सैन फ्रांसिस्को खाड़ी क्षेत्र में 17 उच्चतम-भुगतान वाले शहर

ソフトウェア エンジニアにとって、サンフランシスコ ベイエリアで最も給料の高い 17 の都市

17 cidades mais bem pagas na área da baía de São Francisco para engenheiros de software

17 thành phố trả lương cao nhất trong khu vực vịnh San Francisco cho các kỹ sư phần mềm

旧金山湾区软件工程师薪酬最高的 17 个城市

Written by:  Tyler Crain Introduction Alluxio implements a virtual distributed file system that allows accessing independent large data stores with compute engines like Hadoop or Spark through a single interface. Within an Alluxio cluster, the Alluxio master is responsible for coordinating and keeping access to the underlying data stores or filesystems (UFS for short) consistent. The master contains a global snapshot of filesystem metadata, and when a client wants to read or modify a file, it first contacts the master. Given its central role in the system, the master must be fault-tolerant, highly available, strongly consistent, and fast. This blog will discuss the evolution of the Alluxio master from a complex multi-component system using Zookeeper to a simpler and more efficient one using Raft. The operation of a file system can be thought of as a sequence of commands performed on the system (e.g., create/delete/read/write). Executing these commands one at a time in a single thread gives us a sequential specification for our file system that is easy to reason about and to implement applications on top of. While this sequential specification is simple, the actual implementation of Alluxio’s virtual distributed file system consists of many moving parts all executing concurrently: the master who coordinates the system, the workers who store the file data and act as a cache between the client and the UFS, the UFSs themselves, and the clients who, at the direction of the master, access all other parts of the system. Again here, we may want to reason about each of these individual components as a sequential thread executing one operation at a time, but in reality, they are running complex systems themselves. The core problem we want to solve here is how to make the Alluxio master seem like it is being run by a single node running a single operation at a time so that it is simple to implement and easy for other components to interact with, yet still be fault-tolerant and highly available. Furthermore the master must be fast enough to perform tens of? thousands of operations per second and scale to support the metadata of hundreds of? millions of files. The Journal and HA (Highly Availability) The internal state of the Alluxio master is made up of several tables containing the filesystem metadata, the block metadata, and the worker metadata. The filesystem metadata consists of an inode table that maps each directory and file’s metadata (its name, parent, etc) to a unique identifier called the inode id, and an edge table that maps directories to their children using the inode ids. The actual data of each file is split into one or more blocks located on the Alluxio workers which can be located using the block metadata table which maps each file to a set of block identifiers and the worker metadata table which maps each worker to the list of blocks it contains. Each operation that modifies this metadata, for example creating a new file on the virtual file system, outputs a containing the modifications made to the tables. The journal entries are stored in the order they are created in a log, thus satisfying our sequential specification of the master. Starting from an initial state and replaying these journal entries in order will result in the same state of the metadata, allowing a master node to recover after a failure. To ensure the journal does not grow too large, snapshots of the metadata will be taken periodically. Now if this journal is stored in a location that is highly available and fault-tolerant, then we have a basis for our reliable system. For example, we can start a single node and assign it the role of master. Some time later, if we detect that the node has failed, then a new node is started, it replays the journal, and takes its place as the new master. One possible issue with this is that it may take a long time to start a new node and replay the journal, during which time the system is unavailable. To increase the availability of the system, we can have multiple replicas of the master running at the same time where only one is serving client operations (call this master the ), while the others are just replaying journal entries as they are created (call these masters the ). Now when the primary master fails, a secondary master can take over immediately as the new primary master. journal entry primary master secondary masters Implementing Our Initial Design From this basic design, there remain two problems to solve, 1: How to ensure there is a single primary master running at all times, and 2: how to ensure the journal itself is consistent, fault tolerant, and highly available. Starting in 2013, we introduced the initial implementation to resolve these two problems. The first is solved by Zookeeper [1]. Zookeeper is a highly reliable distributed coordination service providing a filesystem-like interface running over the ZAB [2] consensus algorithm. Zookeeper provides a leader election [3] where multiple nodes can join the recipe and a single node will be chosen as the leader. If a leader is no longer responsive for a certain period then a new leader will be chosen. In Alluxio all masters start as secondary masters and join the Zookeeper leader election recipe, when the recipe elects a node as leader it can then to primary master. recipe step-up To solve the second problem and ensure the journal is highly available and fault-tolerant, the journal entries are stored on the UFS. Of course a UFS that is available and fault-tolerant should be used. Finally, the journal must be consistent. As described earlier the journal entries should be a totally ordered log of events satisfying our sequential specification. As long as there is no more than one primary master running at a time it can just append journal entries to a file in the UFS to satisfy consistency. But doesn’t our leader election recipe already ensure this? If we have a perfect failure detector or a synchronous system, then yes, but unfortunately we have neither. When Zookeeper performs a leader election it will notify the previous leader it has lost its leadership before electing the new leader, but if the previous leader (or the network) is slow, it may still be appending entries to the journal when the new leader is elected. As a result, we may have multiple primary masters appending to the journal concurrently. To reduce the chance of this happening, a mutual exclusion protocol is run using the consistency guarantees of the UFS by using a specific file naming scheme. Let us examine some of the issues of this solution. First, it relies on several external systems, Zookeeper and a UFS, running alongside the Alluxio masters. This complicates the design and analysis of the system. Each component has a different fault and availability model to consider. The choices between different UFSs also complicate the system, for example, one UFS may not provide high performance with frequent small append operations. Furthermore, each UFS may have different consistency guarantees, complicating the case when two concurrent Primary Masters are trying to write to the journal. For these reasons, HDFS is the recommended UFS for this configuration. We now describe how a simpler and more efficient design can be made using Raft. An Improved Design Based on the Replicated State Machine First let us introduce the concept of a ( . A state machine takes as input a series of commands which modify the internal state of the system and may produce some outputs. For our case, the state machine must be deterministic, namely given the same set of inputs, the system will produce the same outputs. If we think of our journal as a state machine, we can think of it as simply a log which has an append command that appends a journal entry to the end of the log. A is then a state machine that provides high availability and fault tolerance. It does this by replicating the state machine over multiple nodes and running a consensus algorithm to ensure the same commands are run in the same order at each replica. Raft [4] is one such replicated state machine protocol, a user just needs to provide a deterministic state machine as input to the protocol. One important property about Raft is that it ensures the consistency condition of [5]. Consider that each command executed on the replicated state machine by a client has an invocation and response. Linearizability ensures that an operation is observed by all clients to have taken place at some instant in time between the invocation and response. This instant in time is called the operation’s . The linearization points of all operations give us a total order of the operations following the sequential specification of the state machine being implemented. So even though our state machine is replicated, to the client it appears to be executing operations sequentially in real-time. replicated) state machine replicated state machine linearizability linearization point Implementing the Journal using Raft Now we could simply provide the log of journal entries as the state machine to be replicated by Raft, but we can simplify the system further. Recall that the reason for representing the state as a log of journal entries was to allow masters to recover to the most recent state by replaying the journal entries. Since Raft takes care of fault tolerance, we can use a higher level of abstraction for our state machine that represents the state of the master. This is simply the collection of tables containing the state of the metadata of the Alluxio file system. The master will execute its normal operations, and provide to the statemachine commands that represent modifications to the tables, which are clearly deterministic. Raft internally handles the logging, snapshotting and recovery of the state in case of failures. The Raft code runs alongside the Alluxio code on the master nodes allowing quick access to the metadata in the tables stores. The tables are implemented using RocksDB [6]. RocksDB is an on-disk key-value store implemented using a log-structured merge-tree [7] which allows for efficient updates. By using RocksDB the size of the filesystem metadata can grow larger than the memory available on the master nodes. Alluxio additionally implements an in memory cache above RocksDB to ensure reading keys is fast. Like before, a single master node will be designated the primary master which serves the client requests. This is because the virtual file system operations that create the journal modifications should only happen at the primary master as they may modify the external state (the UFS and worker nodes). Raft internally uses leader election as part of its consensus algorithm which Alluxio uses to appoint a primary master. To decrease the likelihood of two primary masters running concurrently, Alluxio adds an additional layer of synchronization allowing the previous primary master to explicitly step down or timeout before the new primary master starts serving client requests. Using Raft has greatly simplified the implementation and improved the performance and scalability of the highly available and fault-tolerant masters of Alluxio. The running of the external systems of Zookeeper and a UFS has been replaced with the Apache Ratis [8] implementation of Raft running directly on the Alluxio master nodes. The concept of a low-level log to be replayed after recovery is no longer needed. Instead, we only need to access fast key-value stores using RocksDB, which are efficiently and consistently replicated by Raft to ensure high availability and fault tolerance. We also recommend reading how Confluent simplified and improved the performance of their distributed architecture by moving from Zookeeper to Raft in Apache Kafka [9]. References [1] Hunt, Patrick, et al. “{ZooKeeper}: Wait-free Coordination for Internet-scale Systems.” 2010 USENIX Annual Technical Conference (USENIX ATC 10). 2010. . https://zookeeper.apache.org [2] Junqueira, Flavio P., Benjamin C. Reed, and Marco Serafini. “Zab: High-performance broadcast for primary-backup systems.” 2011 IEEE/IFIP 41st International Conference on Dependable Systems & Networks (DSN). IEEE, 2011. [3] Apache Curator Leader Election. https://curator.apache.org/curator-recipes/leader-election.html [4] Ongaro, Diego, and John Ousterhout. “In search of an understandable consensus algorithm.” 2014 USENIX Annual Technical Conference (Usenix ATC 14). 2014. . https://raft.github.io [5] Herlihy, Maurice P., and Jeannette M. Wing. “Linearizability: A correctness condition for concurrent objects.” ACM Transactions on Programming Languages and Systems (TOPLAS) 12.3 (1990): 463-492. [6] RocksDB. . http://rocksdb.org [7] O’Neil, Patrick, et al. “The log-structured merge-tree (LSM-tree).” Acta Informatica 33.4 (1996): 351-385. [8] Apache Ratis. .[9] Apache Kafka Made Simple: A First Glimpse of a Kafka Without ZooKeeper. . https://ratis.apache.org https://www.confluent.io/blog/kafka-without-zookeeper-a-sneak-peek/ Also Published Here

How Alluxio Stores File System State with High Availability, Fault Tolerance

What is app integration? Application integration is the process of allowing independently developed apps to communicate with one another. What is an Application Integration Tool, and how does it work? Software that merges and optimizes data flows between two different software applications is known as application integration (or app integration for short). Application integration software is frequently used by businesses to build a bridge between a new cloud application and an existing on-premises application, allowing a wide range of independently developed apps to function together. What does App Integration do? Application integration services allow many apps to share processes and business data. This entails seamlessly linking several on-premise and cloud apps to convert and orchestrate the data needed for business activities. These apps can range from CRM and e-commerce platforms to financial and ERP systems, and they can handle a variety of corporate objectives. Why do we use app Integration? The simple answer is that linked systems are essential for providing a complete perspective of standard business activities, particularly those that need several technological inputs and generate income. Most apps, created at different times and by other vendors, do not connect by default and require different technologies. It's also integration technology connecting SaaS and on-premise apps to your corporate architecture, allowing for open communication and removing data silos that bog down company operations. Here are some of the top integration tools for businesses and other customers Boomi Boomi is a subsidiary of Dell Technologies. AtomShere, Boomi's main solution, facilitates the integration of cloud platforms, software-as-a-service applications, and on-premise systems. The configuration of application integrations in AtomSphere is done using a visual interface. Boomi Atom, the solution's runtime tool, enables integrations to be deployed wherever they are required. The AtomSphere platform comes in a variety of versions, depending on the use case and capabilities. Celigo Integrator.io, a Celigo offering, is an Integration Platform as a Service. Organizations may use the solution to link apps, synchronize data, and automate operations. Celigo has an API assistance, a visual field mapping interface, and drop-down options in its integration process. Users may also develop their own library of reusable, independent flows using the tool's reusable pre-configured integration templates, which are offered on the integrator.io marketplace. Quickwork Quickwork is a no-code, real-time, enterprise iPaaS with 1,000+ pre-integrated apps to build enterprise and consumer workflows, publish APIs, and manage conversations. With users in 130 countries, Quickwork is becoming the go-to iPaaS for enterprises. Enterprises use Quickwork to build simple and complex workflows, create and publish secure APIs, and manage conversational interactions with customers, employees and partners to provide a great user experience. Flowgear Flowgear is an integration Platform as a Service company located in South Africa that provides corporate application integration capabilities. Their solution offers a balance of technical complexity and ease of use for developers. The data transportation framework, cloud-based task hosting, and visual designer are all features of Flowgear, which is cloud-native. DropPoints are lightweight services that allow on-premise applications to connect to cloud-based systems. The integration lifecycle may also be approached at two speeds (bimodal) with Flowgear. Informatica For a variety of corporate use cases, Informatica's data integration tools portfolio covers both on-prem and cloud installations. Advanced hybrid integration and governance features are combined with self-service business access to different analytic functions by the vendor. Informatica's CLAIRE Engine, a metadata-driven AI engine that uses machine learning, allows for enhanced integration. Informatica's increasing line of data management software packages boasts high compatibility. Jitterbit Jitterbit is a company that specializes in cloud data integration and API translation. Jitterbit Harmony, the company's principal product, allows companies to design, implement, and manage the whole integration lifecycle. The platform includes a graphical interface for guided drag-and-drop configuration, pre-built templates for integration, and the ability to inject artificial intelligence into applications. The solution may be used in cloud, hybrid, or on-premise systems, and it can feed aggregated data to real-time analytics. MuleSoft MuleSoft is a B2B application delivery network that uses APIs to link data, apps, and devices. The provider provides API access to a wide range of on-prem and cloud-based apps and systems, allowing enterprises to better their applications through integration. MuleSoft has an expanding capabilities portfolio and offers both conventional and Integration Platform as a Service offerings. Benefits of App Integration The advantages vary from eliminating redundant work to improving operations and even cost savings. We have chosen seven of them to show to you below. Your company's control and relationship Application Integration is a fantastic approach to having total control over your business. This is because, thanks to its many methods, information management has improved since it provides an overall view, allowing for full awareness of what occurs inside it. It streamlines corporate procedures. If there's one area where application integration technologies perform, it's in It streamlines corporate procedures. If there's one area where application integration technologies perform, automating and streamlining all help firms. It makes no difference if the technology system is complex or several asynchronous apps. Integration of applications reduces costs and investments. The deployment of application integration also aids in cost savings within the organization. Integration of new and old software is a good illustration of this: there are fewer investments because the former does not need to be deleted. There are fewer investments. Furthermore, business application integration allows for the creation of new services and products that may be applied both internally and outside. This is a fantastic way to save money at all costs in the long term in the long term. It promotes the interchange of knowledge. The information interchange is substantially higher when the various sections of a firm and their separate applications and IT structures are connected. The data and software utilized by the organization are accessible to all workers. This eliminates information redundancy, allowing the organization to function more effectively and efficiently. It reduces the time and effort needed. All business information can be found in one place thanks to the integration of different software systems, making the task of every employee much easier. As a result, the time and effort necessary to execute tasks decreases. Thanks to application integration, large-scale tasks that often take a long time may now be performed in less time. Consequently, the company runs at maximum efficiency, and unnecessary tasks are no longer necessary.

7 Best Application Integration Tools to Consider for 2022

We have all heard the adage “Data is the new oil” in the 21st century. Every business needs to collect, manage, integrate, and analyze the data collected from various sources. Without studying the data properly, no business can earn huge revenues. To churn out the full potential of data, online media offers several data integration software. The software on this list not only help in managing data but also helps in increasing employee efficiency. Let us learn more about data integration software in this post. What is Data Integration Software? Data integration software connects two or more applications by syncing or transferring data from one app to another, making the data more valuable for a business. It helps in sharing the information seamlessly inside any business organization and allows different teams to work together efficiently. For instance, by using data integration software, you can automatically update the data collected from an email campaign into a Google Spreadsheet (in the proper format) and can further use it for lead generation and other activities included in marketing funnels. Benefits of Using Data Integration Software Every business needs data integration software to efficiently utilize and manage data collected from various sources. There are several benefits of data integration software. Let us explore a few of them. Makes complex B2B data integrations simple Enables an enterprise to manage the data in one place Facilitates data of every format Integrates data from multiple sources like emails, social media accounts, and much more Helps in discovering the hidden potential of collected data Offers opportunities for better collaborations among software Automates the routine task like that of manually entering the data Helps in a better decision-making process. Top 9 Data Integration Software There is numerous data integration software available online. Based on public reviews and various rankings, we have categorized the top 9 data integration software for you. Zapier Appy Pie Connect Xplenty MuleSoft Informatica Integrately IBM App Connect Dell Boomi Cleo Integration Cloud 1. Zapier Zapier is a data integration software that can connect your applications and automate several workflows. It automatically syncs the information between web apps and allows you to focus on the most important work processes. Some of the major features of Zapier are as follows. Integrates hundreds of apps and helps automate their workflows Offers a user-friendly platform for DIY automation Provides flexible pricing plans for every business Assures that the tool is completely secured, and data is safe Offers pre-built workflows to learn about automation quickly. 2. Appy Pie Connect Appy Pie Connect is an innovative and intelligent tool to connect various applications and automate various workflows. You can easily select any app from their App Directory and integrate it with another app for successful data integration. Appy Pie Connect ranks among the Top 5 of G2’s cloud data integration software categories. Some of the major features of Appy Pie Connect are as follows. Offers numerous connecting tools to integrate simple projects into complex workflows Ensures that data shared between apps is completely safe and secure Allows businesses to set triggers and actions to automate various workflows Provides powerful editing tools for integrating web and mobile apps Fills the data gaps between sales, marketing, and customer care services Offers user-friendly and intuitive interface for connecting various apps. 3. Xplenty Xplenty is one of the most advanced data integration platforms. Many of their clients explained that Xplenty offers such a flexible platform that they actually enjoy doing data migration. Some of the major features of Xplenty are as follows. Offers a complete toolkit for integrating apps and automating workflows Provides rich expression language for implementing complex dataflows Utilizes the power of SaaS applications to automatically transfer data Offers no-code and low-code platforms to create workflows Provides API component for performing advanced level of customization. 4. MuleSoft Mulesoft is one of the most advanced and comprehensive data integration tools that is utilized to connect data, web-applications, and smart devices with APIs. It allows the development of faster and more affordable solutions for data integration. Some of the major features of MuleSoft are as follows. Offers advanced features across various technologies like iPaaS, ESB, etc. Provides various tools to make cloud-based integrated application systems Utilizes reusable assets for creating integrations Deploys lightweight and scalable runtime engine for integrations Ensures high-level automated security and protection from threats. 5. Informatica Informatica is one of the largest cloud platform providers that support a wide network of system integrators. It helps you explore the power of data across your organizations by integrating various platforms and fetching data from them. Some of the major features of Informatica are as follows. Offers a comprehensive and modular platform for building data integrators Utilizes advanced meta-data driven Artificial Intelligence capabilities Allows users to reuse their data integrators across cloud platforms and processing engines Reduces complexity in creating the programs for automating the transfer of data Provides tools for data integration processing for a large volume of data. 6. Integrately Integrately is one of the most trusted data integration tools utilized by thousands of businesses. It helps in integrating a huge number of applications with pre-built automation workflows. Some of the major features of Integrately are as follows. Offers highly efficient tools to easily integrate two applications Provides a platform to design workflows from scratch Helps in automating a number of routine tasks of businesses Allows businesses to generate leads and maintaining data efficiently Offers user-friendly and highly intuitive interface. 7. IBM App Connect IBM App Connect is an all-in-one software for integrating apps and connecting the data that need to be transferred. It allows you to sync data between both on-premises and cloud-based applications. Businesses all across the world have trusted the IBM App Connect and have been using their tools for many years. Some of the major features of IBM App Connect are as follows. Offers tools to efficiently integrate apps without worrying about mismatched formats Combines data assets across all the applications of an enterprise Offers pre-built templates/connectors to immediately sync data between apps Supports an extensive range of various integration patterns Deploys iPaaS to increase the performance of integration. 8. Dell Boomi Dell Boomi is the highly recommended data integration tool that lets you overcome the complexities faced by IT Teams. Boomi helps in breaking down data silos and integrating on-premises and cloud applications. Some of the major features of Dell Boomi are as follows. Provides a drag-n-drop user interface to create various integration patterns Recommends various data flows to simplify the integration process Offers an extensive library of in-built integrators Consists of multiple highly efficient SaaS solutions Provides tools for data mapping and resolving errors. 9. Cleo Integration Cloud Cleo Integration Cloud is meant to provide end-to-end solutions for data integration. It helps in modernizing cloud integration platforms by providing numerous innovative tools. Some of the major features of Cleo Integration Cloud are as follows. Eliminates the complexities faced in integrating application for data synchronization Establishes secured connection for data transfer Supports every data format by providing an efficient transformation engine Improves efficiency of cloud integration systems Enables data-driven decisions with the help of real-time alerts. Summing Up This was a list of top data integration software. You can select any one of them depending on your business needs. Make sure you try two-three software before selecting the one for final use. It’s high time that you should understand the importance of data integration software and deploy the best one to your enterprise.

9 Best Data Integration Software in 2022

The year of 2021 came to an end, which means it’s time for us to have a look at all the ups and downs that global business has gone through, thus making essential conclusions to be prepared for 2022. 2021 Breathes Life into World Business 2021 has been rough on world businesses, testing their endurance and readiness to deal with the devastating aftereffects of the 2020 world pandemic and forcing everyone to adapt to a new reality. have become the norm of our ordinary way of living. For the last 2 years, we have been trying to crawl out of the hole of despair to get things back on track. And it seems like 2021 was able to give us a light of hope for the upcoming improvement. Global crisis, remote work, overwhelming public restrictions, and uncertainty Facing the disruptive consequences of 2020 made us rethink the way our world functions and opt for decisive moves to withstand the times of darkness. Each and every company had to go online to keep afloat, realizing the need for as they became key elements of survival in our new reality. The proliferation of software development made the IT industry a shining star on the world market. Since so many businesses had to go digital-only, the amount of data has increased tremendously, bringing some entrenched tech trends to the forefront and, at the same time, paving the way for new ones. digital acceleration and software elaboration have become inalienable in a modern digital world, as they help to store information without any barriers. In order to boost cloud technologies and facilitate the remote work process, many businesses have started to incorporate . Another burning trend, which is closely connected to data optimization is the which helps to empower cloud-based applications and transfer data over the network. With the dissemination of cloud technologies, has faced new challenges since the amount of digital data which should be protected has drastically increased (as well as the number of cyber-attacks). Cloud systems AI technologies Internet of things (IoT), cybersecurity Apart from IT trends, 2021 has also brought to life . new opportunities for business enlargement have gone to the next level, as about 2,850 businesses raised more than in 2021. According to , the IT sector has seen the biggest amount of IPOs this year, enabling more and more startups to break through to the global tech arena. Among the most of this year are , and . IPOs $600 billion in IPOs EY outstanding tech startup IPOs GlobalFoundries, GitLab, Wise, Confluent, Braze, Informatica Amplitude Another booming trend of 2021 is which are prevailing in the IT field, reaching an estimated . In such a way, the tech sector now constitutes 20 percent of the M&A market. M&A deals US$888.2bn As we can see, 2021 has been pretty generous with possibilities for business regeneration, ? but what to expect from the upcoming 2022 Key Tech Trends of 2022 The forthcoming year is expected to be promising as the business recovery is already starting to take off. More companies will accelerate the digital transformation, resulting in the following tech trends. People-Centric and Location-Independent Anywhere Operations Since the beginning of the pandemic, the whole world shifted to remote work, making huge changes in the way of doing business. Some prudent companies have already started to take action, preparing for remote work to remain as a permanent fixture in the following years. They have been investing resources into the development of – a business model designed to support customers, employees, and services from any part of the world. Anywhere operations are extremely profitable for companies, as they help to . According to Forrester, around . anywhere operations reduce costs, improve productivity, access diverse pools of professionals, and enlarge business capabilities 70% of large firms will embrace anywhere-work in 2022 Hybrid & Multi-Cloud Computing Trends have been utilized for quite a long time. During the pandemic, cloud systems have become even more widely used due to the fact that they help to (which has become the norm). However, it’s predicted that in 2022 ordinary cloud systems will no longer be relevant as businesses elevate their digital capabilities. Therefore, will become the new trend, which will to help companies enhance their productivity. Such tech giants as Microsoft and Amazon see a bright future in this new technology because it’s predicted that the . Cloud technologies facilitate remote work hybrid & multi-cloud computing combine public and private clouds market size of hybrid cloud will grow to $97.64 billion by 2023 AI Technologies Come in Handy has become a real trend in 2021, and it won’t slip backward in the following year. In 2022 the boom for AI technologies will be in inflated demand, as more and more businesses use them for to ameliorate web products. Gartner estimates that the AI software . AI development analytical data collection, customer support, and web-page optimization market will reach $62 billion , with global spending on it rising to over $57 billion in 2022 The Evolvement of Cybersecurity Strategies has been a burning issue for some time with the increasing number of various websites, platforms, apps, as well as the rapid digitalization processes amplifying the need for enhancing cybersecurity in 2021. The upcoming year will see the need for enhanced information protection as the number of companies going online-only expands. Therefore, will be devised to beat the sophisticated hacker programs. According to the research by Juniper, the . It is also predicted that more and more companies will technologies to in the digital world. Privacy protection new cybersecurity strategies global cybersecurity spend will reach nearly $135 billion in 2022 embrace AI create a better protection model Overarching WebAssembly Technology in Programming The rising need for web development with full-fledged calculations resulted in the elaboration of a new technology – , which will be a . Web assembly will allow developers to utilize any programming language while writing codes. It will substantially increase the speed of web platforms and expand web development technologies. One of the tech giants, Microsoft, has already been investing hugely in its , as this technology looks extremely promising. WebAssebly ruling trend in 2022 Blazor WebAssembly Internet Of Behavior – New Towering Trend (IoB) is an emerging trend that will become more prominent in 2022. IoB enables companies to track people’s behaviors when using various apps and websites and analyze these data to . Embracing the knowledge of behavioral science, edge analytics, and the IoT, this new technology will become a real magic wand for companies to remain competitive in the fast-paced global market. Gartner predicts that . Internet of Behavior devise effective marketing strategies 40% of the worldwide population will be exposed to at least one IoB program by 2025 Offshoring of Software Development With all this remote work, the has been gaining considerable momentum, as it helps companies to expand their business capabilities, enter new markets, and get more investment. This booming trend will only intensify in the upcoming year since more and more companies are looking for new sources of business upscaling. According to Forbes, - due to its huge pool of gifted coders, convenient location, and low pricing. It’s worth mentioning that is gradually suppressing the traditional IT outsourcing / outstaffing as it provides more cost-effective solutions for rapid business enlargement. According to a report by ReportLinker, the R&D outsourcing market is expected to , which means that product companies should start considering software R&D outsourcing right now to preserve their competitiveness in the near future. offshoring of software development CEE region will become a number 1 destination for IT outsourcing in 2022 R&D outsourcing grow by $7.22 bn during 2021-2025 So, the year 2021 has changed our perception of doing business, resulting in numerous challenges and at the same time giving hope for some rapid recovery down the road. What’s crystal clear is that the impending year won’t be uneventful but chock full of outstanding tech trends and budding opportunities for businesses to escalate. Thus, bear that in mind if you don’t want to be left in the dust of the global business race.

Top Tech Trends to Influence Business Recovery in 2022

There’s an ever-increasing reliance on technology in today’s workplace. That’s good news for IT professionals, whose skills are in great demand and whose work can often be done remotely. According to the Bureau of Labor Statistics, . This is a much faster rate than the average for all occupations. About 531,200 new jobs are projected to be added during this time frame. computer and information technology jobs are projected to grow 11% between 2019 and 2029 IT jobs are typically remote-friendly, too. And, if is any indication, remote work isn’t going anywhere. When asked, 65% of respondents said they want to keep working remotely full-time when the pandemic is over. our recent survey We’ve compiled a list of 20 companies that commonly hire for work-from-home IT jobs, along with some recent job postings. Check out these remote IT job opportunities, and if you’re a FlexJobs member, log in to your account and to see the full postings. Note: FlexJobs is a subscription service for job seekers that features flexible and remote jobs. With an A+ rating from the Better Business Bureau , the monthly subscription costs allow us to fully vet and verify all of the jobs on our site —ensuring that customers have a safe and positive job searching experience. Companies That Hire for the Most Remote IT Jobs 1. Cisco A global technology solutions firm, Cisco has become a powerhouse of internet and communications products, services, and solutions that have revolutionized the way business is conducted. Recent remote IT jobs: Senior Systems Engineer Senior Infrastructure Operations Engineer – Compute Senior IP Network Technical Lead 2. Cognizant Softvision A division of Cognizant, Softvision’s goal is to continuously improve its clients’ digital maturity, combining skill, scale, and cost in each stage of the product development lifecycle to create unforgettable digital experiences. Recent remote IT jobs: Solution Architect Mobile Device Management Engineer 3. ConsenSys A blockchain venture production studio based in Brooklyn, New York, ConsenSys builds and scales consumer-centric enterprise software products and tools powered by Ethereum, a public, blockchain-based, open-source distributed computing platform and operating system featuring smart contract functionality. Recent remote IT jobs: Technical Support Engineer, Multi-Lingual Technical Support Engineer Help Desk Agent 4. CrowdStrike CrowdStrike provides cloud-delivered security technology, threat intelligence, and endpoint protection for blue-chip companies in multiple industries. Recent remote IT jobs: Escalation Engineer – Services Engineering Red Team Tool Developer Security Analyst 5. Fastly Internet company Fastly provides the Fastly Edge Cloud platform, a content delivery network that provides clients with improved web and application performance, quality video and streaming, and cloud security. Recent remote IT jobs: Senior Director, Hardware Systems Engineering Senior Provisioning Engineer Senior IT Systems Engineer 6. Gartner Gartner, an information technology research and advisory company, delivers technology-based insights to help companies and organizations around the world make efficient, high-quality business decisions. Recent remote IT jobs: Consulting Associate Director, IT Strategy, Data and Analytics IT Services and Software Analyst Security Programs Director 7. General Dynamics General Dynamics is a global aerospace and defense company. The company has businesses in aerospace, combat systems, information technology, marine systems, and mission systems. Recent remote IT jobs: Dynamics 365 Operation and Maintenance Engineer Senior Appian Developer – DevSecOps 8. GitHub GitHub is an open-source code-hosting website, publishing service, and social networking site for programmers. It has hosted more than 66 million projects and supports a community of over 24 million people. Recent remote IT jobs: Staff Product Security Engineer Staff Security Engineer – Operations and Telemetry Business Systems Analyst, People Systems 9. Kelly Kelly has provided workforce and staffing solutions since 1946. They place workers in diverse industries, including information technology, life sciences, call centers, and more. The company also provides outsourcing and consulting services. Recent remote IT jobs: IT Project Manager – Healthcare Database Architect IT Technical Writer 10. Kforce Kforce is a publicly-traded staffing and recruiting firm that provides staffing solutions in the technology, financial services, healthcare, communications, and government sectors. Recent remote IT jobs: Cloud Operations Engineer Desktop Support Technician Technology Manager – Database 11. Motion Recruitment Partners The parent company of three recruitment agencies, Jobspring Partners, Sevenstep, and Workbridge Associates, Motion Recruitment Partners places people in computer & IT, software development, writing and editing, and graphic design jobs. Recent remote IT jobs: Senior Security Engineer Lead Systems Administrator 12. NTT Group NTT Group consists of regional, long-distance, international, mobile, and data communications businesses. Their mission is to provide high-quality, value-added services that enrich their clients in the areas of artificial intelligence, big data, and the internet-of-things. Recent remote IT jobs: Incident Manager COBOL Developer Data Backup-System Integration Specialist 13. Rackspace Rackspace Technology is a leading managed cloud company dedicated to helping businesses harness the power of cloud computing without the cost and complexity of having to manage it on their own. Recent remote IT jobs: Cloud Architect – Rackspace Government Solutions ServiceNow Business Analyst ServiceNow Architect 14. Randstad One of the biggest staffing firms in the U.S., Randstad provides HR solutions to clients in the accounting, engineering, IT, human resources, and logistics sectors. Recent remote IT jobs: IT Project Manager Informatica Developer Systems Analyst 15. Red Hat Red Hat is an award-winning technology firm. The company develops a broad range of solutions and provides services that are not only open and secure but trusted by over 90% of the Fortune Global 500. Recent remote IT jobs: Software Maintenance Engineer Senior Business Systems Analyst Senior Solution Manager – Senior Product Manager 16. Robert Half International Robert Half is a staffing and consulting firm that places job seekers in a variety of career fields, including computer & IT, administrative, and accounting and finance. The company consists of eight divisions and has over 400 global locations. Recent remote IT jobs: Sharepoint Developer Database Administrator Developer Network Manager 17. Tanium Tanium products help companies with IT security and systems management. Their platform offers 15-second visibility and control of all endpoints on some of the most complex and largest networks in the world. Recent remote IT jobs: Senior Manager, Web Operations Senior Director, Product Management – Security Operations Senior QA Engineer 18. Twilio Twilio is a Platform-as-a-Service (PaaS) company operating as a cloud communications company. Users can make and receive calls using texts and web service APIs. The company also provides clients with privacy and security for messaging. Recent remote IT jobs: Senior Information Security Compliance Analyst Technical Onboarding Manager 19. Unisys Using secure, reliable, advanced technology, Unisys provides clients in the commercial, government, and financial services industries with solutions to enhance the lives of their customers and citizens. Recent remote IT jobs: Enterprise Architect Storage Administrator Cloud Security Engineer 20. UnitedHealth Group UnitedHealth Group consists of two businesses: UnitedHealthcare (a benefits and insurance provider) and Optum (a provider of technology and information-enabled health services). The company provides benefit services and healthcare coverage in all 50 states and in 125 countries worldwide. Recent remote IT jobs: Server Administrator Senior IT Project Manager Senior Director, Quality Operations and Analytics Landing a Work-from-Home IT Job With FlexJobs FlexJobs has a whole category dedicated to , with six subcategories: flexible computer and IT jobs Computer Security Database Administration Networking SQL System Administration Technical Support All of our job listings are fully vetted and verified, giving you a safer, faster, and better job search experience. Previously published at https://www.flexjobs.com/blog/post/top-companies-hiring-for-remote-it-jobs-v2/

20 Companies With Remote Tech Jobs

As companies transform many of their core systems we feel it’s important to speak to our experiences with big master data management technologies that make vastclaims, and have giant invoices behind those claims. Being on the services end of these technologies, we quickly gained perspective from both sides; the promises made and the realities associated with moving forward without a sound strategy that is aligned with anorganization’s 5 to 10 year plan. Most importantly, no strategy or technology will succeed without thoughtful leadership and acceptance of the need to change. The cost of change is very real in opportunity loss, but for how long is it acceptable? Is it possible to measure a rolling ROI of the processes needing improvement in order to justify the effort before a transformation begins and again after changes are in place? As we continue to learn and build methods of measurement, lets review some of the key features of the technology side of Master Data Management solutions. – Virtually all business organizations own data that is living on systems that don’t talk to each other. As a result of these silos of data, it is common to find an increasing number of data duplications and variations. MDM tools are equipped with algorithms that identify duplications and assist in linking data accurately. Linking Data – It is an objective of MDM to manage data according to business rules. When data is modified per standard business rules, sharing data across multiple platforms becomes easy. Therefore, if one rule changes, its impact can be seen everywhere. Centralized Data – Data localization is a process that restricts data within the physical boundaries of a device or place. The free flow of data is restricted to encourage security across the data. Each government authority has set up a localized data funnel to promote data security. Through MDM integration, data restrictions can be imposed efficiently. Localized Data It also falls under the responsibility of master data management solutions to ensure that data coming from various sources is fully enriched. It is a crucial element to cleanse and streamline data to perform a specific task. Thus, refining data and maintaining high quality is one of the primary objectives of MDM, and maybe the most important. Enriched Data – Types of Master Data Management is the process of sorting information, it can be done in any number of ways. Data can be sorted based on cost, transactions, reference, and so on. Therefore, data management can be implemented in different types, but commonly following types of master data management categories are used: MDM master data management – Information based on market data such as purchased cost, sales cost, or any other significant cost is recorded. Mostly, businesses are interested in knowing the monetary data; consequently, this type of management is often practiced. Monetary – Data paired with millions of rows contains more value when compared to the data linked to one or two rows. To sort a large number of data, this type of management is preferred. Carnality – Data has a definite life cycle. Once this period is over, the data won’t remain significant for the organization.  Thus, managing data as per the life cycle helps in getting rid of irrelevant data after a significant period. Periodically – Data can be fixed or variable. To align fixed data doesn’t require master data management. However, variable data needs to be defined properly; therefore MDM is required. Volatility – One fragment of data can be used to perform multiple operations. Typically, customer preference data can be used by sales, marketing, and some other departments also. Accordingly, the data should be presented in a form that all the relevant parties can easily use. Reusability Master Data Management Principles are the foundation of vrious business decisions so the roots of master data need to be coined around solid principles. You should formulate your master data on the following vital pillars: Master data management solutions – The output of an operation is dependent upon its input. If you feed the right input, you will receive the desiredoutcome. This principle of master data management is noted as “data integrity.” If data retrieved from different departments is inaccurate, you can’t rely upon the reports generated around the bad data. This is why maintaining high standards of data integrity or quality is vital. Data Integrity – is a closely knitted principle to data integrity that ensures the continuity of data integrity. Data governance is onlyas valid as the integrity of all the data entered into the system well after the MDM solution is in place. Data Governance – The data never completely changes. Although, if a company must make some changes to the data, a person should be assignedaccountability in each department to enforce the change. Accountability is essential to safeguard the integrity of data. Data Accountability – Auditing isn’t the only part of the finance department; it is also a crucial part of the MDM system. It is usually implemented as a follow-up process after making a change. It helps in recognizing the impact of the change. Data Auditability Benefits of Master Data Management MDM master data management assists in defining the end-to-end journey of an organization. From data collection to data reconciliation, MDM plays a vital role in every aspect of data management. Therefore the list of MDM benefits include numerous highlights such as – – The master data management tools streamline the data and eliminate all the bad data. Users can work with the improved quality of the data and reuse it freely. Accelerated Quality – Without MDM solutions, the companies can’t increase the data’s volume. MDM automates the master data management process which saves lots of time and resources of the business organizations. MDM can significantlyreduce the need for manual labor in the company. Cost and Time Efficiency – Data duplication is one of the major concerns with decentralized data allocation methods. MDM solutions are built on a single data source that can easily solve the problem of duplication. Eliminate Redundancy – MDM weeds out inconsistencies and repetitions from the data. One discrepancy in the data can affect numerous areas of business. Thus, it is important to eliminate bad data from the very beginning to enhance data accuracy. Enhanced Accuracy – Data management offers a holistic vision and centralized control over the data. With the elimination of incomplete data, management can confidently make pivotal business decisions. The quality and effectiveness of decision making can be elevated with MDM. Improved Decision Making Comparison Between Top-7 Master Data Management Tools Master data management software effectively implements the process and facilitates users in performing various operations. In the market, an array of different master data management tools are available, however the Top-7 MDM software applications in Tallgrass research and experience are compared below. SAP Master Data Governance SAP is a consolidated and centralized governance tool that accelerates the consistency and efficiency of information. It is a data management tool for enterprises with a motive to increase accuracy and reduce costing. SAP supports all the leading master data domains and can be deployed on a private cloud or on-premises. Oracle MDM Oracle MDM solution adheres to all the commercial users’ needs. The governance and publication of data seamlessly across the internal and external applications with Oracle multidimensional reach are possible. IBM InfoSphere MDM IBM InfoSphere Master Data Management deals with all the aspects of enterprise data. Usersapplaudthe insightful, actionable, and compliance features of this master data management software. With the hybrid cloud environment, InfoShere MDM orchestrates data throughout its lifespan. Informatica Informatica has been ruling the data management market for 25 years, with 9,500 customers across 82 countries. This MDM cloud-based MDM solution is trusted by Fortune 100 companies as well. This tool supports cloud platform providers, ISVs, system integrators, and more. EnterWorks According to the Gartner Report, EnterWorks is the management product that comes with a 360-degree analytics strategy. Overall, userscan get everything to create, organize, and distribute data – with this one tool. Reltio With Reltio, userscan experience some dynamic features including sales efficiency, market segmentation, compliance management, customer insights, and communication preferences, etc. It is compact tools for business organizations that offer so much more than data management. Innovative Systems, Inc. Synchronos This is an enterprise-level MDM tool to perform operational and analytical tasks. Companies can bypass the extremely large and time-consuming approach with the high tech features of Synchronos. For instant services, userscan trust this tool. Conclusion Master data management solutions are one of the integral parts of a modern business organization. Without adequate tools to manage, align, and analyze large volumes of data, no company can Regardless of how large or small a business operation you are running, you need to find the right master data management tools to empower your business’s decision-making power. make effective data-driven decisions . Don’t be surprised if the best choice might be the one you already have. Keep in mind all MDM technologies never stop evolving, it’s by design that they are rigid enough to establish standard but pliable enough to evolve. It takes a highly functional team to understand the difference. Having decades of enterprise wide experience call on use for an expert third party perspective before you risk your precious time and money!

Master Data Management – Strategy or Technology?

Bridging the gap between Application Developers and Data Scientists, the demand for Data Engineers rose up to 50% in 2020, especially due to increase in investments in AI-based SaaS products. After going through multiple Job Descriptions and based on my experience in the field , I have come up with the detailed skill sets to become a competent Data Engineer. If you are a Backend Developer, some of your skills will overlap with this list below. Yes, it's quite easier for you to make the jump provided the skill gaps are addressed. 🎯 Must Haves 1️⃣ The Art of Scripting and Automating Can't stress this enough. Ability to Write reusable code and to know the Common Libraries and frameworks used in Python for: * Data Wrangling operations - Pandas,numpy,re * Data Scraping - requests/BeautifulSoup/lxml/Scrapy * Interacting with External APIs and other Data Sources, Logging * Parallel processing Libraries - Dask, Multiprocessing 2️⃣ Cloud Computing Platforms The rise of cloud storage and computing has changed a lot for data engineers. So much that, being well versed in at least one of the cloud platforms is required. *Serverless Computing, Virtual Instances, Managed Docker and Kubernetes Services * Security Standards, User Authentication and Authorization, Virtual Private Cloud, Subnet Either start with or services. AWS GCP 3️⃣ Linux OS Importance of Working with Linux OS is often overlooked. "90% of the public cloud workloads are running on Linux based OS" * Bash Scripting concepts in Linux like control flow, looping, passing input parameters * File System Commands * Running daemon processes 4️⃣ Database Management - Relational Databases, OLAP vs OLTP, NoSQL * Creating tables, Read,Write,Update and Delete operations, joins, procedures, materialized views, aggrgated views, window functions * Database vs Data warehouse. Star and snowflake schemas, facts and dimension tables. Common Relational Databases preferred - PostgreSQL, MySQL etc 5️⃣ Distributed Data Storage Systems * Knowledge of how distributed data store works. * Understanding the Concepts like partitioned data storage, sorting key, SerDes, data replication, caching and persistence. Some of the mostly used ones - HDFS, AWS S3 or any other NoSQL database (MongoDB, DynamoDB,Cassandra) 6️⃣ Distributed Data Processing Systems * Common techniques and patterns for data processing such as partitioning, predicate pushdown, sort by partition, maintaining size of shuffle blocks, window function * Leveraging all cores and memory available in the cluster to improve concurrency. Common Distributed processing frameworks - Map Reduce, Apache Spark (Start with Pyspark if you are already comfortable with Python) 7️⃣ ETL/ELT tools and Modern Workflow management Frameworks Different companies will have different ways to pick ETL frameworks, One with an In-house data engineering team would prefer to have ETL jobs set up with properly managed workflow management tools for Batch Processing. * ETL - ETL vs ELT, Data connectivity, Mapping, Metadata, Types of Data Loading * When to use a Workflow Management System - Directed Acyclic graphs, CRON scheduling, Operators ETL Tools: Informatica, TalendWorkfow Management Frameworks: Airflow, Luigi🎯Good To Have 8️⃣ JAVA / JVM Based Frameworks Knowledge of a JVM based language such as Java or Scala will be extremely useful - Understand both functional and object oriented programming concepts - Many of the high performance data science frameworks that are built on top of Hadoop usually are written using Scala or Java. JVM Based Frameworks - Apache Spark, Apache Flink, etc 9️⃣ Message Queuing Systems * Understanding how data injestion happens in Message Queues * What are Producer and Consumers and how are they implemented * Sharding, Data Retention, Replay, de-duplication Popular Messaging queues: Kafka,RabbitMQ, Kinesis, SQS etc 🔟 Stream Data Processing *Differentiating between Real-time, Stream and Batch Processing. *Sharding, Repartioning, Poll Wait time, topics/groups,brokers Commonly used frameworks: AWS Kinesis Streams,Apache Spark,Storm,Samza etc If you find any other skill that will be helpful, let me know in community. Going forward, I'll publish detailed posts on tools and frameworks used by Data Engineers day in and day out. Follow for updates. @bufferpoint Also published on: https://dev.to/srinidhi/data-engineering-series-1-10-key-tech-skills-you-need-to-become-a-competent-data-engineer-2n46

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