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Data Management and Consolidation in the Integration of Corporate Information Systems

Power-up: Machine Learning and Data Engineering (R)evolution for Optimizing Marketing Efforts

Business intelligence has become an indispensable part of successful businesses, and the sooner executives recognize data as a crucial component of decision-making, the sooner they will be able to improve their operational processes. Back in 2015, the BI and analytics software market was estimated at $17.90 billion, while in 2020 this market is to grow up to $26.78 billion, reports. The numbers are impressive, and they are another proof of the growing interest in data analytics—companies all around the globe are either investing in BI solutions now, or are in search of perfect partners and solutions to invest in. MarketsandMarkets Each year Gartner publishes their Magic Quadrant—a list of front-runners in particular technological verticals, rated according to the most important criteria for buyers on a particular market. In 2020, Gartner’s Magic Quadrant for Analytics and Business Intelligence Platforms includes Tableau among the Leaders for the eighth consecutive year, second only to Microsoft. This is due to several factors: Now with Tableau as part of Salesforce, its adoption rates are accelerating worldwide. Tableau inspires both organizations and employees to adopt a data-driven culture. It is a multifunctional end-to-end platform that is user-friendly and can be integrated with various data sources. A trend that has proven to be effective, Tableau has become a preferable option for many companies. But its wide range of editions, features, and add-ons has made Tableau pretty difficult to implement by non-professionals. That’s where Tableau consulting companies come in—providing qualified services in implementing the platform and teaching your team how to use it, such companies make data even more accessible and facilitate the task of building a data-driven culture. As mentioned above, data is in high demand nowadays, and the analytics services market does not lag behind: there are hundreds of companies delivering Tableau consulting, so choosing a reliable one may appear to be a challenge. If you are currently looking for such a partner, we would like to make the task easier for you by providing a list of 10 best Tableau consulting companies. Analyzing the market, we have identified five most important differentiators as the basis for the rating: Experience Flexibility Price policy Portfolio Online reviews With their own journey and success stories, these are all market leaders who provide competitive consulting services. You are free to choose the one which seems to be perfect for your particular business based on the scores below. Best Tableau Consulting Providers to Consider in 2020: 1. Accenture A US company providing their services worldwide, Accenture is one of the top Tableau consulting companies on the market. With a team of more than 492,000 experts, they operate in 52 countries, including the US, Singapore, Israel, and South Africa. Accenture was listed among Forbes’ Global 2000 and Fortune’s Global 500 in 2019. In addition to delivering a number of strategy- and technology-related services, Accenture has an alliance with Tableau. With the help of the platform, the company aims at helping businesses implement BI and data visualization solutions to understand data and improve their processes. 2. Iflexion Evolved from a team of 10 experts into a leading Tableau consulting company on the international market, Iflexion has been providing high-quality services since 1999. Now a team of 850+ engineers, they focus on such IT services as web and mobile development, integration, QA, and consulting. The company has rich experience in working in a number of domains, including Ecommerce, Security, Media, Finance, and more. With headquarters in Denver, CO, Iflexion operates globally, having worked with more than 500 clients from 30 countries. Their expertise comprises Tableau roadmapping, development, migration, customization, training, and support. Helping businesses turn data into insights, Iflexion has been named one of the top B2B Service Providers according to Clutch, including in 2019. The list of their clients includes many renowned names, in particular eBay, adidas, Philips, Expedia, and other. 3. Ideas2IT Ideas2IT began as an engineering company focusing on Silicon Valley startups in 2008. Since then, they have become established data experts and won a leading position among the best Tableau consulting companies. Today, the team of 240 devoted professionals operates from the USA and Switzerland, with offshore offices in India, Canada, and Ecuador. Ideas2IT specializes in the blockchain technology, IoT, and data science. The latter comprises application development, data modeling and wrangling, DevOps, and consulting, including their vast experience in Tableau consulting. The company focuses on Healthcare, Finance and Retail industries, and has worked with such enterprises as Oracle, Microsoft, and AirAsia. 4. Wipro Wipro is an Indian company established as a part of Western India Products in 1945. However, today it’s an independent public company with 175,000 professionals on board and offices on six continents, including in the US, Canada, Australia, and Thailand. Wirpo focuses mostly on Automotive, Retail, Banking, and Utilities domains. The company delivers high-quality IT solutions, including data analytics, DevOps, software engineering, and consulting. The company has proved to be an expert in data-related services, including Tableau consulting, and was nominated for a number of awards at IT World Awards. 5. Sojourn Solutions Founded in 2005, this is a team of experts providing services in marketing operations and automation. Their expertise includes marketing technologies based on the most popular BI platforms, and this is due to the excellent quality of the company’s Tableau consulting services that they are on this list. Sojourn Solutions delivers Tableau-based analytics, reporting, and managed services. Operating from the US, UK, and Poland, the company consists of 54 devoted professionals who have worked with a number of enterprises. Some of their clients are Vodafone, O2, and Progress Software Corporation. 5 More Tableau Consulting Companies to Keep Tabs On: 6. Deloitte and Tableau Alliance Deloitte is a company that has been providing audit and consulting services for more than 100 years now. Throughout this time, the company has gained international recognition as one of the best business partners in the world. In 2019, there were 90,000+ professionals working in Deloitte’s teams in 100+ countries all over the globe. Today, as a professional Tableau consulting services provider, Deloitte comprises 5,000 Tableau experts who’ve successfully completed more than 100 projects in this domain. The list of the company’s clients includes Adobe, Dell EMC, Yamaha, and other well-known names. 7. Softweb Solutions Inc. With more than 10 years of experience and offices in the US and India, Softweb Solutions is one of the leading data-related service providers on the market. Their expertise comprises machine learning and AI modeling, data engineering, advisory, governance, and consulting. The latter includes Tableau consulting—the company has already successfully integrated more than 300 data sources with Tableau. With 500+ employees on board, Softweb Solutions has worked with Bosch, PepsiCo, Siemens and other enterprises, having delivered 1,400 data-oriented projects in total. The company focuses on Retail, Healthcare, Banking and Insurance industries. 8. InterWorks This company has been providing IT consulting services since its inception in 1996, and has grown into an international company with offices in nine countries, including the US, UK, Australia, and Netherlands. In addition to various data management, analytics and strategy services, InterWorks has an official partnership with Tableau, which makes it one of the most reliable Tableau consulting providers. With more than 200 employees, InterWorks focuses on such industries as Healthcare and Education. Their Tableau expertise comprises project planning, server consulting, ad-hoc analysis, and more. 9. Unilytics Unilytics has 18+ years of experience in BI analytics and has been a Tableau partner for six years already. They focus on various Tableau consulting services: training, dashboards creation, product integration, and more. In addition to this, the company delivers a number of solutions in digital analytics and data management. Unilytics has managed to build 1,200 Tableau dashboards and trained 2800 Tableau users. The list of their clients includes Project Management Institute, Union Pacific Railroad, and Alcatel. Unilytics works in various domains, such as Public Sector, Education, and Retail. 10. WCI Consulting Located in five US states and Canada, WCI Consulting has been delivering consulting solutions to international clients for 22 years now. Their vast experience includes working on all types of data-related projects, providing analytics services and custom engineering solutions. The company has a team of skilled professionals who specialize in Tableau consulting. They also provide solutions based on other most popular BI platforms, such as Dell Boomi, Microsoft Azure, and Talend. Though WCI has worked with many industries, most of their projects are connected to Healthcare, Manufacturing, Banking, Retail and Energy domains. In particular, some of the notable names on their client list are Fujitsu, Shell Oil, and Pizza Hut. Stay Tuned!

Top Tableau Consulting Companies on the Market in 2020

An Encryption Deep Dive - Part Three

Dashboard software has become a necessary tool for businesses. With all the information you must gather and monitor about your business, you may wonder how to get the most out of it. Actionable insight can help you gain a competitive edge, considering the gap between data and business value. In fact, in 2023, achieved measurable value from and analytics investments, which reinforces the need for data-driven decision-making. 91.9% of organizations data to ensure you understand your data and benefit from it. Dashboard software merges data from linked databases and provides a customizable and interactive platform for data visualization Additionally, it empowers everyone in your company, from a data analyst to a junior manager, allowing them to visualize, present, and use data in any way that makes sense to them. In this article, we’ll explore the most eminent benefits of dashboard software and how you can leverage them to get the most out of your investment. 1. Effective Data Visualization Businesses gather huge amounts of data. Understanding such amounts of data can be challenging and time-consuming, to say the least, necessitating the need for tools that can help organize your data in a manner you can comprehend. When this is not possible, your business may experience challenges in making decisions and executing them in time. How can you make decisions based on data communicated ineffectively? Similarly, . This leads to a lot of time wasted trying to organize and present huge amounts of data. without data visualization, your employees may not know where to focus their attention to improve productivity One of the benefits of dashboard software is its ability to transform your business data into easy-to-understand graphs, images, and charts, which are essential considering that the human brain processes visual information more readily than textual information. Actually, the use of visual language is proven to enhance effectiveness and efficiency, leading to 24% shorter meetings. It is safe to conclude that this statistic is only possible because visualization enables data to be easily communicated and understood. The software interprets and simplifies the data for you to help generate true value for your business. It becomes easier to make decisions, recognize patterns and errors, and improve data analysis. GenerationE is an IT solutions provider that captures and automates best practices for IT operations by up to 85%. InetSoft provided a visualization layer for GenerationE, helping the company and saving its team the effort of charting and analyzing log data in external reporting systems. monitor the performance of automated processes 2. Improves Decision Making Great data, when analyzed correctly, can help enhance decision-making. It is important to gather and analyze business data to understand key business metrics and your performance to help you make better decisions to upscale. Without good dashboard software, your business may experience challenges collecting and managing data as it is scattered across multiple databases. Even if you can access this data from siloed systems, moving and analyzing it manually takes time and can impede accurate reporting and fast decision-making. Slow and inefficient decision-making can negatively impact your business in 2024 by costing you missed opportunities, time, and money. Dashboard software merges data from different sources and provides immediate access to at-a-glance information, ensuring you make decisions quickly to avoid missing out on opportunities. Additionally, with clear, actionable insights, you can make more accurate data-driven decisions and create business-boosting strategies to give you a competitive edge. In fact, Insight-driven businesses are growing at an average of , according to a report by Forrester. 30% each year This figure summarizes the when it comes to decision-making. Data in the digital era is extremely critical, and it helps to have the right tools to help you leverage. benefits of dashboard software Kreditech is a financial company based in Germany that offers clients loans based on their creditworthiness. Data analysis and decision-making are therefore critical. After incorporating Datapine, Kreditech was able to decentralize decision-making and achieve faster and smarter data-driven decisions. Thanks to the software’s friendly interface, users were able to work with zero IT overhead. 3. Increases Business Efficiency Enhancing business efficiency can help grow your business. It is undeniable how critical business efficiency is to grow and survive in the current economic climate. Without dashboard software, your business risks inefficiencies due to siloed information, manual data entry processes, ineffective data organization, and presentation. To err is human, so inefficiencies can frequently occur without the right tools to streamline business intelligence operations. Manual processes can also lead to data errors that are more prevalent when dealing with large amounts of data. In fact, Gartner found out that organizations believe poor data quality to be responsible for per year in losses. an average of $15 million For starters, reducing manual tasks is a key component for successful business management and plays a huge role in increasing efficiencies. It is a no-brainer that automation technologies like dashboard software can achieve more than a human and deliver more efficient results through workflow automation. A dashboard software offers smart, autonomous data management solutions to help you save time and money by gathering quality insights. Additionally, dashboard software brings data from different sources into a central respiratory system, eliminating the need for multiple external solutions. Therefore, you can be able to take quick action on your data-driven insights to upscale your business. Parcero is a Columbian marketing agency that was looking for a solution to help save time on manual processes. After implementing Klipfolio, Parcero was able to automate client reporting and enhance efficiency. They recorded in repetitive advertising tasks and 73% in repetitive reporting tasks. 33% time savings 4. Empowers End-Users Dashboard software empowers your employees to be self-reliant. The amount of time wasted waiting for data analysts and IT teams to present and explain data is a lot. This is not to mention that it distracts them from focusing on their job to advance the business’s goals. Unfortunately, there remains a huge gap between having data and being able to use it. Even with huge amounts of data, most businesses need help to take action on the valuable insight because they cannot interpret it. In fact, struggle with utilizing their , according to Talend. 78% of executives data for decision-making Imagine how much you could achieve if your employees could access and interpret data on their own without relying on data analysts. Well, one of the in business intelligence is empowering end-users by providing a simplified and clear platform where they can access data and manipulate it in a way that makes sense to them. benefits of dashboard software This ensures enhanced productivity, prompt decision-making, and minimized costs thanks to the ability to get answers independently without needing help from the BI or IT team. This also allows your IT team to focus on their job without being distracted by frequent help requests. GMS is a leading distributor of construction products based in Georgia. The company was looking for an easy-to-implement solution to help users access valuable insights. Thanks to InetSoft, GMS users can based on data consolidated into interactive reports that are easy to read. make quick business decisions 5. Reduces Operating Costs Dashboard software can minimize business costs. Without a great dashboard software in 2024, data remains fragmented across multiple systems and databases, impeding prompt decision-making. Siloed data can result in expensive operational costs caused by inefficiencies, analysis, and manual data collection. . Without , your employees may spend hours fetching and trying to interpret data. Time is money, and when you are losing both, you could be laying a foundation for your business’s failure the right tools In fact, Airtable’s research revealed that knowledge workers reported spending nearly 29% of their week (11.6 hrs) searching for the key information they need to do their work. Conversely, implementing dashboard software provides a central repository that combines and organizes data from different sources to provide clear visibility of key metrics. . It also provides a unified view that lets you easily identify cost-saving optimization opportunities and budget allocation opportunities In addition to that, dashboard software capabilities such as automation help to eliminate manual and repetitive tasks that can be time-consuming. Consequently, this reduces the chances of inefficiencies and errors that could cost you money. Dashboard software significantly reduces operational expenses by identifying inefficiencies promptly, optimizing resource allocation, and making data-driven decisions, ultimately contributing to substantial cost savings. Just Spices is a German-based company that manufactures spices. The company had been investing a lot of time in extracting and analyzing data for a while. With Klipfolio, Just Spices as they could access and visualize key metrics that show different departments where help is needed instead of having people extract data. saved time and money Conclusion In the digital age, data is extremely essential for businesses. How you use the business data you gather can influence the success of your business. However, with vast amounts of data, it can be challenging even for experts to organize and leverage data to get the best out of it. Dashboard software benefits such as reduced costs, enhanced efficiency, and clear visibility into key metrics can set your business apart. Actually, according to Capgemini, say its company’s Big Data initiatives are profitable. 27% of business executives Investing in a great dashboard software in 2024 can empower small businesses with enterprise-level insight without exaggerated costs.

5 Benefits of Dashboard Software for Business Intelligence in 2024

Mastering Strategic Integration of Corporate Information Systems 

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

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