Kannan was previously the Head of HR for Retail Asset, Corporate Functions, Technology and the Head of People Engagement at Kotak Mahindra Bank, preceded by senior roles at Bajaj Finance, Fujitsu Consulting, Oracle Financial Services, Mindtree and Symbiosis College, according to a statement.

IIFL Finance appoints Preeti Kannan as CHRO, HR News, ETHRWorld

Super Micro Computer, Inc. (NASDAQ:SMCI) has entered a long-term strategic partnership with Fujitsu Limited to develop and market a platform that will feature Fujitsu’s future Arm-based “FUJITSU-MONAKA” processor. The platform is designed for high performance and energy efficiency and ...

Supermicro And Fujitsu Partner For AI-Powered Server: What's In Store?

Japanese firm <strong>Fujitsu</strong>&#x27;s faulty software was behind more than 900 subpostmasters being wrongly prosecuted after shortfalls were incorrectly reported on their accounts.

Post Office 'pens £180m deal to keep Fujitsu's Horizon system in place for another FIVE YEARS' despite faulty software scandal which led to more than 900 wrongful prosecutions and several suspected suicides | Daily Mail Online

The existing contract with <strong>Fujitsu</strong> to run Horizon is due to conclude in March 2025, and the aim was to have a replacement system, called New Branch IT (NBIT), in place by that time. However, delays, cost over-runs and a lack of quality in the software being developed in-house by the Post Office ...

Fujitsu set for further £180m deal as Post Office Horizon replacement delayed | Computer Weekly

The study includes market share ... Hewlett-Packard, Hitachi Ltd, Oracle Corporation, Unisys Corporation, Super Micro Computer, Toshiba Corporation, Fujitsu Ltd & Dell....

Enterprise Server Market Is Dazzling Worldwide with Major Giants Hitachi, Unisys Corporation, Fujitsu, Dell - EIN Presswire

/PRNewswire/ -- HFR Networks Inc., leading the industry with intelligent xHaul RAN Transport and Edge Access solutions today announced interoperability between...

HFR Networks Announces Integration with Fujitsu Radio Units

Exclusive: Whistleblowers say Paula Vennells agreed to move archive, which risked destroying data that could clear operators

Ex-Post Office boss ‘gave Fujitsu bonus contract despite warnings’ | Post Office Horizon scandal | The Guardian

Exclusive: FoI request shows minister was warned by civil servants of ‘risk’ but focused instead on tech firm’s investment in UK

Kemi Badenoch failed to raise Horizon scandal when she met Fujitsu at Davos | Post Office Horizon scandal | The Guardian

Japanese telco KDDI, Cisco Systems and Fujitsu announced the recent launch of operations of a metro, regional network that combines the IP layer network and optical transmission layer network for streamlined operations. In a release, Fujitsu explained that ...

KDDI, Cisco and Fujitsu start operation of metro network in Japan

Fujitsu

fujitsu

The State of The Market — January 31, 2019 : $3,462.79 (-0.59%) : $0.308250 (-2.68%) : $106.74 (-1.99%) BTC XRP ETH As January comes to an end, the crypto market is still stagnant. After yesterday’s gains, the entire market is flashing red right now. All of the top 50 cryptocurrencies have posted losses. The total market cap lost about $2 Billion in the last 24 hours. No recovery is in sight yet, as Bitcoin continues to trade below $3,500 and is struggling to go past its resistance at $3,800. In other news, a recent report published by Bloomberg suggested that the . 125 crypto venture funds launched in 2018 whereas only 115 new crypto hedge funds launched in 2018. Industry figures interviewed by Bloomberg explained that weakness in the ICO market prompted the shift toward venture capital investment. Also, . Alderoty had previously worked at CIT Group as the executive vice president, general counsel, and chief legal officer and now he will manage Ripple’s global legal policy and Bank Secrecy Act (BSA) compliance teams. The hiring appears to be a strategic maneuver as Ripple is currently fighting a federal class-action lawsuit brought forward by investors who claim XRP is an unregistered security. launch of new crypto venture funds exceeded the number of new crypto hedge funds Ripple announced that Stuart Alderoty has been hired as the company’s new legal counsel SWIFT CEO Gottfried Leibbrandt made the announcement at a panel on stage. However, very little was revealed as SWIFT is expected to make a separate announcement soon. Leibbrandt was on stage with Ripple CEO Brad Garlinghouse when he made the announcement. Ripple is a direct competitor to SWIFT as both companies operate in the settlement of international transactions. R3 itself has integration with XRP. In December, the company launched Corda Settler to facilitate cryptocurrency payments within enterprise blockchains, starting with Ripple’s XRP. ( ) 1) Global banking payments network SWIFT has announced its plans to integrate blockchain technology provided by R3 at the Paris Fintech Forum. R3 is well-known for its Corda blockchain platform. Read More Fujitsu successfully tested the system with support from electric power distribution company ENERES and recorded 40% improvement when compared to the existing system called Demand Response (DR). The system ensures stable electricity supply during peak periods. The platform incentivises consumers who shift their usage during peak periods. The blockchain platform also matches available electricity to buy orders with fast response times. Back in October, Fujitsu developed a blockchain-system for inter-bank settlements. ( ) 2) Japanese IT giant Fujitsu has revealed a blockchain-based electricity exchange system for enterprise customers. Read More When Bitcoin’s lightning network was launched last year, it was still an experimental technology which could lose money. For this reason, developers implemented a limit of 167.77216mBTC (approx. $600) which can be locked into a channel. However, the off-chain scaling solution has exploded into a reliable service with over $2 Million stashed away. For this reason, lightning network developers have enabled an option called “option_i_wumbo_you_wumbo.” Users can choose it while opening a channel to bypass the $600 limit. ( ) 3 Lightning Network has removed its biggest capacity limitation. Read More Subscribe to the Berminal Brief Newsletter Download Berminal App for Free The Berminal Website Berminal Official Telegram Berminal Twitter

SWIFT Partners With R3, And Fujitsu Reveals Blockchain Electricity Exchange

Why 96% of Agile Transformations Fail - Here's Why You Shouldn't Bother

Yuan Yi Zhu, an Assistant Professor of International Relations and International Law at Leiden University, recently criticized an article published in The Times of London. The article was written by Matthew Parris, a former Member of the British Parliament. In the article, Parris argues in favor of legalizing euthanasia on the grounds that society simply cannot afford “desperate infirmity for as many such individuals as our society is producing.” Parris argues that it would allow the UK economy to better compete with China and people being coerced to being euthanised due to social pressure is “not a bad thing”. Parris says he does not apologise for how his article "treats human beings as units - in deficit or surplus to the collective". In February 2024, an article was published in the prestigious Scientific Reports journal by Nature, arguing that “AI holds the potential to carry out several major activities at much lower emission levels than can humans.” The piece argues that humans produce far greater CO2 emissions when doing writing or illustration work than AI Large Language Models. The article also notes that: “The freed human time may also incur new, unexpected environmental costs.” Advances in AI haven’t just led to humans being compared to machines but also being compared to animals. Late last year, according to The Guardian, BT’s Chief Innovation Officer, Harmeen Mehta ‘suggested workers whose jobs are threatened by AI accept their fate as “evolution,” comparing them to horses replaced by the car.’ Against this backdrop, one does wonder when we will inevitably see the rise of those who will argue for eugenics such that humans replace machines. This is nevertheless a stark reminder of how humans ultimately are behind killer computers. In a new book I authored called ‘ ,’ I have explored many catastrophic computer disasters. These disasters have included an aircraft entering ‘death dives’, fatal car crashes and lethal radiation doses. How to Protect Yourself from Killer Computers Recently, software reliability has been pushed to the forefront of the public conscience. March 2024 saw system failures, causing McDonald’s outlets in the UK, Australia, and Japan to go offline. Panama Bread in the US saw IT outages, as did the UK high-street giants Tesco, Sainsbury's, and Greggs. As several Boeing executives stood down from their jobs following the death of a whistleblower, it’s worth remembering how the first Boeing 737 MAX groundings began with a computer software problem. Previously unreported, it was also the case that March 2024 saw London police officers report chaos in jails and being unable to begin search warrant applications, as there was also an outage with the Metropolitan Police’s IT systems. The public inquiry into the UK Post Office’s Horizon IT Scandal is underway, a scandal where faulty accounting software resulted in miscarriages of justice, claiming around 700 victims and has been linked to four suicides. This included a pregnant mother being sent to jail. Engineers are now facing the consequences for being complicit in disasters; with FTX’s former Director of Engineering facing up to 75 years in prison, a VW engineer who was complicit in the emissions scandal having been sentenced to 40 months imprisonment despite co-operating with investigators and the architect of the Post Office Horizon IT system currently under investigation by UK police for perjury. In common across all these disasters, whilst the technical factors varied, I found a common theme of wrongdoing, retaliation, and cover-up. Issues were brushed under the carpet instead of addressed directly. For example, one engineering manager at Fujitsu, who built the software behind the Post Office scandal, told the public inquiry how he “was threatened with violence by an individual with a reputation for violence” during one of his early attempts to remedy the issues. This is an industry-wide problem. In November 2023, I led research that found that 53% of software engineers suspected wrongdoing at work, with 75% of software engineers in the UK having experienced retaliation the last time they reported it to their employers. This is a concern as computers do inevitably make mistakes as no software is perfect. Small things like a binary number being flipped (e.g., a “1” becoming a “0”) due to cosmic rays are enough to cause havoc in computer systems, particularly those that haven’t been designed with this risk in mind. There is, however, hope here. The research conducted in the new book finds that resilient systems can be built when humans are part of the system. In these , humans play a role in preventing disaster by raising the alarm and mitigating the issues. socio-technical systems For this to happen, humans must be free to raise the alarm to issues when they come across them. If the on-call engineers who keep our online services and critical national infrastructure running are not able to speak up and act to prevent disaster, these problems emerge. However, this applies to all software engineers and, indeed, everyone who is part of the software development process, including the customers. Killer computers are born in toxic cultures where engineers aren’t able to raise the alarm, and issues are brushed under the carpet. By making sure humans play a key role in the reliability of computer systems, we can help ensure technology serves society.

The Killer Humans Behind Killer Computers

Two years ago, I studied the impact of developer burnout on software engineers, finding . Over recent months, I’ve been working on further research on perceptions of software development for a variety of organisations, including findings that 75% of software engineers and that 89% of business leaders were . 83% suffered from burnout faced retaliation the last time they reported wrongdoing concerned about the on-time delivery of software Google’s DORA team have for several years conducted their own polling of software engineers and the original authors of the measurement framework have now produced other frameworks including SPACE and DevEx. Whilst I originally trusted the research produced by these teams, as I’ve conducted further research the flaws have become evident. Over the holiday period I’ve been reading Dr Andrew Jenkinson’s book "Why We Eat (Too Much): The New Science of Appetite", in the book Dr Jenkinson criticises a study known as the Seven Countries Study by Dr Ancel Keys. Dr Jenkinson describes Dr Keys success as follows: “He had won the argument over his biggest rival, trouncing him with indisputable facts, exposing his flawed logic. The crowd's adulation filled him with joy and ecstasy. His life's work had reached fruition. The funding for his research would come rolling in, his reputation as the leading scientist in his field would be secure for years. Fame was good, but now he had secured the top two real prizes - power and influence.” However, Dr Jenkinson notes: “He had not been dishonest about his research - that would have been unethical and discredited him. Technically what he had presented was the truth. But he knew very well that it was not the whole truth.” As I’ve studied the research outputs of DORA and later work in further detail, the parallels between this description and the research rigour in DORA’s State of DevOps Report and the subsequent SPACE and DevEx framework have become evident. Where’s the Data? Firstly, DORA research is conducted by sampling many thousands of developers through the use of subjective surveys. This research is conducted in-house by the DORA team. Ordinarily, those who conduct such research for a living have joined organisations like the Market Research Society (MRS) and British Polling Council (BPC) to ensure the public can have confidence in the research done by organisations who are members. For example; BPC rules place strict disclosure rules on their members requiring they disclose complete data tables with the questions asked within 2 working days of the research being published. Here rests our first problem; the DORA team does not publish their raw data, only publishing their State of DevOps Report. Flawed Methodology Google’s DORA research, and the SPACE and DevEx frameworks used within team settings, use subjective surveys to create measurements. When using subjective surveys, it’s important to take steps to ensure bias doesn’t come into play. However, DORA uses Four Key Metrics to measure outcomes - Change Lead Time, Deployment Frequency, Change Failure Rate and Time to Recovery (formerly Mean Time to Recovery). These are essentially measures of the speed to get new features deployed and the speed of resolving issues. Imagine you asked some people “Do your colleagues eat lots of greens?” and “Do your colleagues work out a lot?”. Those who feel better about their workplace would probably be more likely to answer “yes” to both questions - this does not mean that eating more greens will always lead to greater levels of gym attendance. Whilst there may be a correlation, we haven’t created a cause-and-effect relationship. DORA research argues that speed and reliability go hand-in-hand, however, they do so based on outcome measures which are entirely based on speed. Moreover, the use of subjective surveys can bias recipients who feel better about their work to answer “yes” to both questions. And whilst companies who are more competent may inevitably be more competent at both factors, this does not create a causal relationship. For example; consider how highly regarded the reliability of aviation software is, versus the infrequent deployment of software to aircraft. Or consider how Toyota, the pioneer of agile methodologies, in the software reliability case “Bookout v. Toyota” on an unintended acceleration bug that led to fatalities conceded in internal communication that "In truth, technology such as failsafe is not part of the Toyota Engineering division's DNA". Or consider how during the Horizon IT scandal - blamed for multiple suicides and what has been described as “the most widespread miscarriage of justice in UK history”, with those wrongly imprisoned including a pregnant woman - the software developer, Fujitsu pioneered the use of an agile methodology to develop software, namely . Rapid Application Development Flawed Measurement Outcomes As discussed, DORA research measured against Four Key Metrics that assess the speed for deploying new work and fixing bugs to evaluate performance. However, these metrics only matter to the extent that they are useful outcomes to measure. I have conducted (with the research firm Survation) and found that both agree speed is the least important factor. Instead, the public cares most about data security, data accuracy and preventing serious bugs. It is hard to find a hypothesis which would connect the Four Key Metrics to these outcomes which software developers and the public say are most important - especially given that preventing serious bugs is outright a lower priority than fixing bugs quickly or getting work fast. Even for other factors like data security, it’s hard to see how these connect to any of the Four Key Metrics. research of both software engineers and a representative sample of the general public Even amongst business decision-makers, it seems that on-time delivery matters above fast delivery. According to research I conducted with J.L. Partners, 98% of such business decision-makers in the UK and 96% in the USA agree with the statement “The goal of a software engineering team is to deliver high-quality software on time”, with 65% in the UK and 62% in the US strongly agreeing. Finally; the research I conducted with Survation found that trust in software engineers and the reliability expectations of the public can vary considerably from industry to industry, meaning a one-size-fits-all approach should be discouraged in favour of what Engineering Council UK suggests in their : “adopt a decision-making approach that is proportionate to the risk and consistent with their organisation’s defined risk appetite”. Guidance on Risk Follow the Money Like Dr Keys received funding from the sugar industry in his research - in many investigations, it’s important to follow the money to understand where incentives lie. The DORA team originally started doing State of DevOps reports for Puppet, a company focussed on automating IT infrastructure and now they do this work for Google Cloud. Both have a vested interest in developers being able to deploy work as quickly as possible. This does not mean however that it is the solution to all our problems. DORA has made a contribution to the world of software engineering in adding a degree of empirical evaluation to the process. However, we must avoid confusing marketing material for the whole truth and recognise the flaws in such research.

Bye Bye DORA: Flaws of the State of DevOps Reports

বাই বাই ডোরা: DevOps রিপোর্টের রাজ্যের ত্রুটিগুলি৷

Bye Bye DORA: Mängel im Stand der DevOps-Berichte

Bye Bye DORA: Defectos del estado de los informes de DevOps

Bye Bye DORA : failles des rapports sur l'état des DevOps

अलविदा डोरा: DevOps रिपोर्ट की स्थिति की खामियाँ

バイバイ DORA: DevOps レポートの現状の欠陥

안녕 안녕 DORA: DevOps 보고서 상태의 결함

Tchau, Tchau, DORA: Falhas do estado dos relatórios DevOps

Прощай, ДОРА: недостатки отчетов о состоянии DevOps

Güle Güle DORA: DevOps Raporlarının Durumunun Kusurları

Tạm biệt DORA: Những sai sót của báo cáo trạng thái DevOps

再见 DORA：DevOps 现状报告的缺陷

Beyond Automation: Charting the Global Impact of Artificial Intelligence

Photo by Rafael Pol on Unsplash In the ever-evolving computing landscape, one concept has become not just a trend but a necessity: parallel programming. The days of exponentially increasing clock speeds driving improvements in single-core performance, as predicted by Moore’s Law, are behind us. Instead, the future of computing lies in the realm of parallelism. Moore’s Law is falling behind :( Picture this: colossal amounts of data pouring in from every corner of our digital world, complex simulations demanding more computational power than ever, and artificial intelligence algorithms hungry for processing capabilities. In the face of these challenges, the single-core processor is no longer the hero of the story. To meet the demands of modern computing, we need to harness the power of parallel programming. In this series of articles, we embark on a journey through the world of parallel programming, exploring its profound significance and practical applications. In this inaugural installment, we’ll lay the foundation by addressing two fundamental questions: and Why is parallelism a necessity, what are the tangible benefits it brings to the table? As we delve into the heart of parallel programming, you’ll discover that it’s not just a choice for optimizing software performance; it’s a requirement in today’s digital landscape. By the end of this article, you’ll gain a deeper understanding of why parallelism is indispensable and how it revolutionizes the way we approach computing challenges. So, fasten your seatbelts as we embark on a journey to uncover the transformative power of parallel programming. A brief history of parallelism Parallel computing, with its roots dating back to the early days of computing, has a storied history. In the early 1970s, the was a pivotal milestone when it was delivered to NASA Ames Research Center. Despite its conceptualization beginning in 1952, construction only commenced around 1966. The remarkable Illiac-IV featured , each functioning as an autonomous mini-computer. It delivered remarkable computational prowess, achieving up to , a groundbreaking record for its time. Illiac-IV a single core housing 64 processing elements in a 4x4x4 cube formation 200 MFLOP/s : 200 MFLOP/s Illiac -IV in 1972 Apple iPhone 8 in 2017: 297 MFLOP/s , a groundbreaking advancement in the realm of high-performance computing, took center stage with the introduction of the iconic supercomputer. While the Illiac-IV excelled in parallelism, the Cray-1 harnessed the transformative potential of vector processing. Conceived by the visionary Seymour Cray in the mid-1970s, this pioneering machine harnessed specialized hardware to execute operations on entire arrays of data within a single instruction. In addition to its 8 general-purpose registers and 8 floating-point registers, the Cray-1 featured , each boasting a 64-bit width, unlocking unprecedented computational power. Vector processing Cray-1 8 vector registers The Cray-1 achieved peak performance, reaching an astounding , setting a new standard for high-performance computing. Cray Research’s subsequent models continued to push the boundaries, with companies like Hitachi and Fujitsu following suit in their own product offerings. Remarkably, vector processing remains as relevant today as it was in its inception, serving as a foundation for modern computing architectures. 240 MFLOP/s Since then, a continuous stream of innovations has transformed the landscape of computing. From the inception of , which interconnected multiple PCs to amplify computational power, to the integration of external accelerators such as GPUs (Graphics Processing Units) and FPGAs (Field-Programmable Gate Arrays), parallel computing has undergone a profound and ongoing evolution. Cluster Computing The TOP500 list is a well-known ranking of the world’s most powerful supercomputers. It is published twice a year, typically in June and November, by a group of international experts in high-performance computing. The list ranks supercomputers based on their performance in a standardized benchmarking test known as the High-Performance Linpack (HPL) benchmark. The Need for Parallelism Moore’s Law and the End of Single-Core Performance Gone are those days when upgrading your processor would speed up your application by 3x. Better or for worse, the single thread performance is saturating, and people keep boasting that ‘ . So, what exactly is Moore’s law? Moore’s Law is Dead!!!’ Moore, who co-founded Intel Corporation, noted that the number of transistors on a semiconductor chip doubled approximately every two years, leading to a consistent increase in computing power while reducing the cost per transistor. Despite being widely called a ‘ ,’ it was an empirical observation and prediction. Essentially, it states that: law Every 18 to 24 months, the number of transistors on a microchip will double, while the cost per transistor will halve. Moore’s Law had profound implications for the technology industry, as it drove the development of increasingly powerful and smaller electronic devices. It became a guiding principle for the semiconductor industry and a source of inspiration for innovation and investment. For decades, semiconductor companies focused on increasing the number of transistors in their microprocessors and the increase in performance followed almost in parallel. However, several factors contribute to the perception that Moore’s Law is ending or at least slowing down. A pivotal turning point occurred around 2005 when it became evident that the linear correlation between the increase in transistor count and single-threaded performance was no longer as pronounced. Simultaneously, CPU clock frequencies had already approached saturation levels, further underscoring the challenges in sustaining traditional performance scaling. So, why is this happening? With the limited space vendors have on their microprocessors, they can only fit a certain number of transistors. Until now, with advancements in nanotechnology and related manufacturing regimes, it was possible to shrink the size of transistors, thereby resulting in more transistor density on the exact size of the board. However, as transistors shrink, they approach the fundamental limits of atomic and quantum physics. When transistors become too small, quantum effects such as tunneling start to dominate, making it challenging to maintain reliable and predictable behavior. Similarly, further miniaturization of transistors requires increasingly complex manufacturing processes and materials. These challenges not only increase costs but also limit the rate of progress. In addition to the economic constraints and feasibility of manufacturing processes, heat dissipation becomes a significant issue. High power densities lead to overheating and reduce chips’ reliability, lifespan, and performance. One perspective on why many people believe that Moore’s law is nearing its end is that the doubling of transistor density no longer means that the cost is getting halved. While this may be a matter of one’s perspective, what truly matters here is that alternatives must be found, and progress made. Data-Intensive and Computationally Intensive Applications Modern applications, including data analytics, AI, and simulations, crave immense computing power. , for example, must swiftly process colossal datasets. This involves sorting, filtering, aggregating, and generating insights from vast information. Parallelism splits these tasks into smaller, concurrent chunks, hastening analysis and enabling rapid data-driven decisions. Data analytics Similarly, , particularly deep learning neural networks, is computationally intensive. It involves processing large datasets and adjusting model parameters through numerous iterations to optimize performance. Parallelism expedites model convergence by distributing computation across multiple cores or GPUs, allowing larger, precise models to be trained within feasible timeframes. training AI models like weather forecasting and fluid dynamics involve intricate numerical calculations. These simulations often involve solving partial differential equations and running simulations over extended periods. Parallelism divides simulations into smaller solvable segments, slashing time and facilitating high-resolution modeling, thus advancing scientific research. Scientific and engineering simulations Big Data and Scalability In recent years, there has been an unprecedented explosion of data generated from various sources, including social media, IoT devices, sensors, e-commerce, scientific research, and more. For instance, social media platforms generate billions of posts, images, and videos daily, while IoT devices continuously collect data on environmental conditions and machine performance. Genomic research and e-commerce platforms also contribute to this data tsunami. This deluge of data is commonly referred to as “big data,” characterized by its immense volume, high velocity, and diverse variety of formats. How do we handle this insane amount of data? Scalability is the capability of a system to handle increasing workloads and accommodate the ever-expanding datasets without compromising performance. In the context of big data, scalability is vital for several reasons. It allows organizations to manage growing data volumes effectively by horizontally scaling and adding more computing nodes to handle larger datasets without performance degradation. Scalable systems also ensure rapid processing to support real-time analytics and decision-making. Furthermore, they offer cost-efficiency by enabling the allocation of resources as needed, reducing upfront hardware costs, and optimizing resource utilization. The combination of parallelism and scalability is essential to tackle the challenges posed by the influx of big data, empowering organizations to extract valuable insights and drive innovation. This brings us to the end of this article. In this post, we discussed briefly how parallelism emerged as a natural choice for extracting maximum performance and how its relevance will keep increasing. In the upcoming posts, we’ll discuss the fundamentals of parallel programming, specifically talking about the theoretical and programming aspects of parallel applications. Until then, keep parallelizing! Also published . here

Why Parallel Programming is a Game Changer

Every company needs a database to store and organize data. Data can be very sensitive, so we always have to be careful while accessing or manipulating it in the database. Here are a few essential you should know as a developer. databases Redis Redis is an in-memory data store. Since the main memory(RAM) read/write operation is faster than the disk read/write ( databases like MySQL, Mongo, and h2 are storing data on hard disk), you can massively improve the performance of your application. Remember, since Redis is in memory, it is volatile in nature by default, but you can configure it to write to disk at regular intervals so if the server fails, you won’t lose your data. Redis is not a replacement for your database. One common use case is using it as the data cache; by loading frequently and accessing data sets to Redis, you can avoid too many reads to your database and can improve the performance of the application. Cassandra The Apache Cassandra database is the right choice of the database if you are looking for scalability and high availability without compromising performance for your mission-critical applications. Easily Deals With Velocity, Variety, and Complexity Issues. Can Handle Massive Datasets. Homogeneous Environment. Ease of Administration. MongoDB MongoDB is a cross-platform NoSQL database. You can use this database for mobile apps, real-time analytics, and IoT, and it can provide a real-time view of all your data. MongoDB is a high-speed database, and the data is stored in the form of JSON-style documents. It has deployment flexibility, high performance, high availability, and easy scalability. MySQL MySQL is a very popular open-source RDBMS that is used by most of the major tech companies, such as Facebook, Google, Twitter, and Adobe. It was acquired by Oracle as a part of Sun Microsystems’ acquisition in 2009.MySQL has different features for different editions (Enterprise Edition, Standard Edition, and Classic Edition). PostgreSQL This database is also an open-source Object-Relational DBMS, but users are free to create NoSQL databases. It is ideal for companies that frequently deal with large volumes of data. A lot of gaming apps, database automation tools, and domain registries use this database. Companies such as Apple (macOS Server operating system uses this database), Cisco, Fujitsu, Skype, and IMDb use this database. Neo4j Neo4j is the first and dominant mover in the graph market. The company’s goal is to bring graph technology into the mainstream by connecting the community, customers, partners, and even competitors as they adopt graph best practices everywhere. Highly Performant Read and Write Scalability Without Compromise. Rock-Solid Reliability for Mission-Critical Production Applications. Whiteboard-Friendly Data Modeling to Simplify the Development Cycle. Elasticsearch ElasticSearch is a search engine based on the Lucene library. It is a distributed and open-source full-text search and analytics engine. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents. A lot of big organizations like Wikipedia, The Guardian, StackOverflow, GitHub, etc. It allows you to create replicas of their indexes and shards. It is scalable up to petabytes of structured and unstructured data. Multi-tenancy and extremely fast search. Designed to operate in real-time. GraphQL (not a database, but something you should know about) GraphQL allows making multiple resource requests in a single query call, which saves a lot of time and bandwidth by reducing the number of network round trips to the server. It also helps to save waterfall network requests, where you need to resolve dependent resources on previous requests. For example, consider a blog’s homepage where you need to display multiple widgets, such as recent posts, the most popular posts, categories, and featured posts. With REST architecture, displaying these would take at least five requests, while a similar scenario using GraphQL requires a single GraphQL request. Also published . here

Essential Databases Every Developer Should Be Familiar With

PostgreSQL and MySQL have been around for decades and have proven highly reliable, secure, and scalable. However, they have different strengths and weaknesses that make one more suitable for specific use cases. This article will compare PostgreSQL and MySQL to help you make an informed decision in 2023. History and Development PostgreSQL was first released in 1996 and has become a widely used open-source RDBMS. It is known for its strong adherence to SQL standards, robust feature set, and focus on data integrity and security. MySQL, on the other hand, was first released in 1995 and has been widely used for web-based applications due to its high performance and ease of use. Over time, Oracle acquired the open-source database management system MySQL and turned it into a commercial product. Features PostgreSQL and MySQL both offer a wide range of features as relational database management systems, but there are some key differences between the two: PostgreSQL supports a broader range of advanced data types, including , and , which provide more flexible and efficient data storage options. On the other hand, MySQL has a more limited set of data types and is geared toward more straightforward web applications. Data Types: arrays, hstore (a key-value store) JSONB (binary JSON) PostgreSQL strongly supports geospatial data, including a rich set of data types, functions, and operators for handling geographic data. MySQL, while it has some geospatial support, could be more robust in this area. Geospatial Support: In MySQL, the default index type is B-tree, which is well-suited for most use cases. PostgreSQL has a more refined indexing system than MySQL, including support for B-tree, GiST (Generalized Search Tree), and GIN (Generalized Inverted Index) indexes. These provide more options for optimizing query performance and data retrieval. Indexing: PostgreSQL and MySQL support database replication, but the methods and options for replication differ. PostgreSQL supports multi-master replication, while MySQL primarily supports master-slave replication. MySQL has recently introduced a new replication model called , but it is still a relatively new feature with some limitations. Replication: Group Replication PostgreSQL and (Multi-Version Concurrency Control) to handle concurrent data access. However, PostgreSQL offers evolved transaction management features such as transaction isolation levels, atomic transactions, and savepoints. In contrast, MySQL's transaction management options are more limited. PostgreSQL may be better for applications requiring high concurrency or complex transaction logic. Transactions: MySQL InnoDB utilize MVCC PostgreSQL and MySQL support stored procedures, but the language and functionality of stored procedures differ. PostgreSQL supports stored procedures written in various languages, including PL/pgSQL, PL/Tcl, PL/Perl, and more. MySQL, alternatively, primarily supports stored procedures written in SQL. Stored Procedures: PostgreSQL has a robust extension framework that allows developers to add custom functionality and extend the core features of the database. While it has some support for extensions, MySQL has a different level of extensibility than PostgreSQL. Extensions: Change Data capture In terms of (CDC), both MySQL binary logs and PostgreSQL write-ahead logs (WALs) can capture changes made to the database. However, the specific features and usage of the CDC may vary. change data capture is a software that can read MySQL and PostgreSQL transaction logs and transform the records to another dialect, making it suitable for heterogeneous database replication in real time. DBConvert Streams Performance is known for its high performance and ability to process large amounts of data. It has been optimized for read-heavy workloads and has a fast indexing system that helps improve query performance. However, concurrency issues such as lock contention can occur when combined with write operations, leading to performance degradation. This is due to the implementation of locks at the table level, preventing all actions while the write operation is in progress. MySQL The InnoDB storage engine is used to solve the table-level locking issue. It is one of the most popular and widely used storage engines in the MySQL ecosystem. InnoDB supports row-level locking, improving concurrency for mixed workloads. Additionally, the recent development of the high-performance storage engine, MyRocks, has further improved MySQL's ability to handle write-intensive workloads. is designed to be more versatile, handling both read-heavy and write-heavy workloads, but with slightly lower performance than MySQL, which is optimized for read-heavy workloads. However, PostgreSQL has improved its performance in recent versions, especially regarding complex queries and data processing. PostgreSQL Additionally, PostgreSQL has a more advanced indexing system compared to MySQL, which can improve performance for complex queries. PostgreSQL also supports advanced data types, like arrays and JSONB, which can result in more efficient data storage and retrieval. Ultimately, the performance of both PostgreSQL and MySQL will depend on various factors, such as hardware, data size, and query complexity. When choosing between the two, consider your application's specific requirements and conduct performance testing with your data and workloads to determine the best fit. Scalability Both MySQL and PostgreSQL can scale, but they have different strengths and weaknesses when it comes to scalability. MySQL is often preferred for its horizontal scalability, which means it can be scaled out by adding more nodes to the database cluster. It is ideal for web applications that need to handle a large number of concurrent connections. On the other hand, PostgreSQL is known for its vertical scalability, which means it can handle large amounts of data and processing power by adding more resources like memory and CPU to a single node. It also supports horizontal scaling through technologies such as sharding, which allows large datasets to be split across multiple nodes. PostgreSQL is preferred for applications requiring complex queries and transactions and for data warehouse and business intelligence workloads. In terms of scalability, consider the specific requirements of your application. If you need to handle a large number of concurrent connections and need horizontal scalability, MySQL may be a better choice. However, PostgreSQL may be better if you require complex transactions and queries. Cost In 2023, it is still true that PostgreSQL is fully open-source and community-driven, whereas MySQL has a more complex history concerning licensing. MySQL was initially developed as a commercial product by MySQL AB, with free and paid versions available. The acquisition of MySQL AB by Oracle in 2010 raised some concerns among developers about the future of its open-source status. However, several open-source forks of the original MySQL, including MariaDB and Percona, have helped mitigate these concerns. When to Use MySQL? While PostgreSQL has many cutting-edge features and is often considered a more advanced and complex database management system than MySQL, it does have its drawbacks. Some of the common weaknesses of PostgreSQL include the following: Despite its advanced features and capabilities, PostgreSQL has yet to reach the level of popularity and widespread use of MySQL. This has resulted in a smaller number of third-party tools and a lesser number of experienced developers or database administrators in the PostgreSQL ecosystem. Due to its advanced features, PostgreSQL can be more complex to set up and manage than MySQL, making it more suitable for experienced database administrators and developers. PostgreSQL can be slower in certain use cases than MySQL due to its more complex architecture and features. PostgreSQL can require more resources than MySQL, particularly in terms of memory and CPU usage. While PostgreSQL is open-source, the cost of implementation and maintenance can still be high due to its advanced features and increased resource requirements. PostgreSQL forks a new process for each new client connection, which can allocate a significant amount of memory, typically around 10 MB per connection. However, this architecture is designed to provide improved security and isolation between different clients and is generally considered a trade-off for better performance, reliability, and scalability. PostgreSQL is designed to prioritize extensibility, standards compliance, scalability, and data integrity. Sometimes, these features can decrease performance compared to MySQL, especially in simple read-heavy workloads. However, it's important to note that the exact performance difference depends on various factors, such as the data's size, the queries' complexity, and the hardware being used. Which migration is more common: MySQL to PostgreSQL or PostgreSQL to MySQL? The migration frequency between MySQL and PostgreSQL varies and depends on individual organizations' needs and requirements. Some organizations may migrate from MySQL to PostgreSQL to take advantage of its advanced features, better SQL compliance, and open-source compatibility. PostgreSQL is also more prevalent in specific industries, such as financial services, government, and data warehousing, where performance, scalability, and security are essential considerations. On the other hand, other companies may migrate from PostgreSQL to MySQL for its simplicity, broad community of support, and lower cost of implementation. The migration trend is towards moving from MySQL to PostgreSQL based on various indicators. These indicators suggest that more people are shifting from MySQL to PostgreSQL compared to the reverse. Many open-source and commercial migration tools are available to assist in migrating data from MySQL to PostgreSQL. At the same time, there are fewer tools to migrate data from PostgreSQL to MySQL. Availability of migration tools: There is greater availability of online tutorials and resources on migrating from MySQL to PostgreSQL compared to the other way around. Online resources: The PostgreSQL community has been growing faster than the MySQL community, indicating a growing interest in using PostgreSQL over MySQL. Community growth: The number of open source contributions to PostgreSQL is increasing, indicating that more people are investing time and resources into the technology and finding it useful for their needs. Open source contributions: Some of the world's largest and most data-intensive organizations, such as Cisco, Fujitsu, and the U.S. Federal Aviation Administration (FAA), have publicly stated that they have moved from MySQL to PostgreSQL. Enterprise adoption: Industry analysts and database experts have conducted surveys that suggest that more people are considering or planning to switch from MySQL to PostgreSQL. User surveys: These facts only indicate that more migrations happen from MySQL to PostgreSQL than the other way around, and it may only be true in some cases. Conclusion. PostgreSQL and MySQL are robust relational database management systems with unique features and limitations. The decision to use one over the other should be based on the specific requirements of a project, such as the nature and amount of data, the intricacy of queries, and performance and scalability needs. As both PostgreSQL and MySQL are set to undergo further advancements in 2023, it is crucial to stay updated on their recent developments. In addition, it's worth mentioning that tools like DBConvert Studio can help with migrating data between MySQL and PostgreSQL in either direction. These tools can simplify the process of transferring data from one database to another, which can be especially useful if you consider switching from one system to another. Also published . here

An In-depth Look Into MySQL Vs. PostgreSQL

A precursory article that explains various categorizations of artificial intelligence, some real-life examples and concepts. Table of contents Introduction Types of Artificial Intelligence Capabilities-based AI categorization Functionalities-based AI categorization Conclusion Introduction Artificial Intelligence has been the hot tech buzz worldwide among computer scientists in the last few years. It is nothing but a branch of computer science involved with the design of computers or other programmed mechanical devices having the capacity to imitate human intelligence and thought. Despite some of the already successful innovations and advanced research, the technocrats claim that the depth of AI that we have seen until now is just like a drop in an ocean, the time is yet to deliver the advanced sophisticated phase of this technology. In this article, I am trying to explain the basic categorization followed in the innovation happening in the AI tech industry which will be a piece of very useful yet foundational knowledge for those students and professionals who are eager about this concept. Types of Artificial Intelligence All of the AI based solutions that have been already developed and those which are in pipeline for the future ideation and implementation are categorized broadly according to two congregations- Capabilities-based AI categorization Functionalities-based AI categorization Under the Capabilities bases AI categorization, we have the following types of solutions- Narrow Artificial Intelligence General Artificial Intelligence Super Artificial Intelligence Under Functionalities-based AI categorization, we have the following types of solutions- Reactive Machines Limited Memory Theory of Mind Self Awareness Capabilities bases AI categorization Cataloguing AI solutions based on their comes under this type of categorization. Let us go through each one of them in detail. "power or ability to do something" Narrow Artificial Intelligence Artificial Narrow Intelligence (ANI), also called Weak AI or Narrow AI is the only type of artificial intelligence we have successfully perceived until today. Narrow AI is AI that is programmed to perform a single task and it cannot perform the tasks beyond its limitation. These systems can solve problems in real time but their entire knowledge about this universe is restricted to the specific datasets that the researchers used while training the models. This makes them unable to solve any problem outside of the patterns recognized from the training datasets. It has no consciousness, sentiment or human emotions. As I mentioned earlier, all of the advanced AI applications designed and delivered in recent times belong to Narrow AI such as- - A multilingual neural machine translation service developed by Google to translate text, documents and websites from one language into another Google Translate English Wikipedia 's homepage translated  into Portuguese (Source: Wikipedia ) - A question-answering computer system capable of answering questions posed in natural language IBM Watson Watson demo at an IBM booth at a trade show (Source: Wikipedia ) - A private intelligent virtual assistant that helps to make calls, send texts, use apps, and get things done with just your voice. Etc. Apple’s Siri Siri Remote for the Apple TV (Source: Wikipedia ) You can obviously doubt why we call these solutions “Weak AI” although the magnitude of the problem they solve is huge and they are derived out of complex algorithmic computations. The reason is that they are still very distant from actual human intelligence. They are deprived of the capacity to think of themselves to solve an actual problem instead, most of the problems and corresponding solutions are coded to their artificial intelligence in a patterned way using various algorithms. For example, We consider Siri as the most advanced private virtual intelligent assistant to date. It can help you in assisting the routine work and let you remember the daily schedules, official or education-related doubts, health-related queries etc. but it cannot answer the questions if you need some advice or guidance related to personal life that your close friend or parents can provide by analyzing rationally, logically and emotionally by understanding your circumstances. Although the lack of understanding of real-life surroundings is considered a limitation of Narrow AI, we must accept its positive side such as the speed that it can provide while solving a problem that a human cannot even think of. They can automate various mundane tasks, give insights about various data and sometimes acts as a helper in various decision-making activities. General Artificial Intelligence General AI, also referred to as “Strong AI” or “Deep AI” is a concept of a machine with general intelligence that mimics human intelligence, with the ability to think, understand, learn and apply its intelligence to solve any problem as humans do in any given situation. These types of AI solutions come along with systems that are conscious, sentient, and driven by emotion and self-awareness. Still, human intelligence is considered superior to Strong AI because humans can strategize plans and creatively think about new ideas, and our thoughts and decision-making ability exponentially grow with the experience while interacting with this universe on a daily basis. These aspects of human intelligence are not yet replicated by Strong AI machines. Although General Artificial Intelligence is lacking consciousness and emotions, it can solve problems and make appropriate judgements under uncertain conditions. Also, it uses its prior knowledge for somewhat imagining various scenarios and making suitable decisions. K Computer (Source: Wikipedia ) Fujitsu, a Japanese Information and communications technology company built one of the fastest supercomputers called the “K computer” which is considered a notable attempt at achieving Strong AI. It took 40 minutes to simulate a single second of neural activity. Hence, this scenario made computer scientists clear that Strong AI is difficult to achieve in the foreseeable future. Riken Advanced Institute for Computational Science (AICS) in Kobe, which housed the K computer (Source: Wikipedia ) Super Artificial Intelligence Super Artificial Intelligence is defined as a form of AI capable of surpassing human intelligence by manifesting cognitive skills and developing thinking skills of its own. Its existence is still hypothetical. It can surpass human intelligence in all aspects ranging from creativity to general wisdom, to problem-solving. Researchers often define a scenario called “Technological singularity” which is nothing but a hypothetical point in time at which technological growth becomes uncontrollable and irreversible, resulting in unforeseeable changes to human civilization. Machines having Artificial Super Intelligence might be able to do certain things that are not humanly possible. If these machines start to emerge then that becomes a singularity point. For example, What if a Super intelligent machine starts to replicate itself by creating similar machines and starts to rule the world by considering humans as their slaves? Photo by Brian McGowan on Unsplash It will have the capacity and knowledge to create weapons, form the government and control us by framing certain rules. Actually, when we reach the singularity point, we won't be knowing how those machines will react to us. Some researchers believe that there are 2 chances in the midst of uncertainty- An artificial super-intelligent machine with good character An artificial super-intelligent machine with a bad character Image by from Lukas Pixabay If the machine is having good character then it will almost become like a God. It will be immortal, It can save humankind from various natural disasters, treat and heal physical diseases, solve political and economical crises, etc. But if the scenario is the opposite then we can't even imagine the degree of disaster that we are going to face. Functionalities-based AI categorization Cataloguing the AI solutions based on their comes under this type of categorization. Let us go through each one of them in detail- “quality of being suited to serve a purpose well” Reactive machines Reactive machines are those AI solutions that cannot form memories or use past experiences to influence present-made decisions. They can only react to currently existing situations, hence the name “reactive”. An AI program created by IBM in the 1980s named “Deep blue” is considered an example of a Reactive machine. The objective of this AI solution was to compete against humans for playing and winning the chess game. One of the two cabinets of Deep Blue in its exhibit at the Computer History Museum , California (Source: Wikipedia ) Although it doesn't learn and improve its performance from past experience since it has no memory, it can optimally make a decision for moving a piece on the chess board according to the rule of games to win against the opponent. Garry Kimovich Kasparov, a Russian chess grandmaster and former World Chess Champion was defeated by this AI program to become the first computerized program to defeat a human opponent. This experiment showed the potential of the “reactive” ability of AI to make quicker, more accurate and optimal decisions based on the environment. Garry Kasparov playing a simultaneous exhibition in 1985 (Source: Wikipedia ) In challenging aspects, Reactive machines don't possess the capabilities to perform tasks beyond their simple programmed knowledge. The lack of memory and no understanding of the past, present and future makes them unfit for solving further complex problems. Limited Memory Limited memory overcomes the challenges faced by Reactive machines in many aspects of problem-solving. It can derive knowledge from previously-learned information, stored data, or events. Although they can store the data, their memory is usually short-lived. They use both programmed logic and patterns recognized and learnt from a lot of trained datasets to solve complex problems. Self-driving cars are considered an example of Limited memory. These cars use their memory while driving for understanding the environment and memorizing the scenario such as- Is there any obstacle coming in front of the car? Is the car keeping enough distance from the pedestrians ? Are there any traffic lights active now ? How other vehicles near this car is placed on the road? etc. A prototype of Waymo 's self-driving car, navigating public streets in Mountain View, California in 2017 (Source: Wikipedia ) These pieces of information are memorised by the AI and it combines this information with already present pre-programmed logic to make appropriate decisions. Theory of Mind Theory of Mind is a kind of AI that aims to understand human needs by detecting emotions, and sentiments, discerning beliefs and mapping thought processes. This technology is in the early stage of research and we haven't achieved its completion yet. Two notable examples are the robots Kismet and Sophia, created in 2000 and 2016 respectively. Kismet, developed by Professor Cynthia Breazeal, was capable of recognizing human emotions based on facial signals and could replicate said emotions with its face, which was structured with human facial features. Kismet now resides at the MIT Museum in Cambridge, Massachusetts , United States. (Source: Wikipedia ) Sophia is a humanoid bot created by Hanson Robotics. She has the ability to see and respond to interactions with appropriate facial expressions. Sophia in 2018 (Source: Wikipedia ) These two humanoid robots are considered successful initial steps for achieving “Theory of Mind AI” in the near future. Self Awareness Self Awareness AI can understand their states, internal traits, and conditions and perceive human emotions. Self Awareness is not currently in existence but would be considered the most advanced form of artificial intelligence that can be imagined by a human brain. They will be able to think for themselves, have desires, and understand their feelings. Image by Gerd Altmann from Pixabay The major difference between the “Theory of Mind” and “Self Awareness” is that the “Theory of Mind” only focuses on the aspects of comprehension and replication of human practices. However, “Self-awareness” takes it a step further by implying that it can and will have self-guided thoughts and reactions. Conclusion In this article, we discussed one of the most important but foundational categorizations of artificial intelligence. Also, we went through some of the real-life experiments that the industry has already extrapolated in the past couple of years. I hope by going through the reference sections of this article, AI enthusiasts will be able to surf through more in-depth contextual and elaborative information. References Poole, David ; Mackworth, Alan (2017). Artificial Intelligence: Foundations of Computational Agents (2nd ed.). Cambridge University Press. ISBN 978-1-107-19539-4 . Russell, Stuart J. ; Norvig, Peter (2009). Artificial Intelligence: A Modern Approach (3rd ed.). Upper Saddle River, New Jersey: Prentice Hall. ISBN 978-0-13-604259-4 .

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