Notes about Cutting-Edge Technologies and Everything

  Database management systems continue their move to the cloud — a move that is producing an increasingly complex landscape of vendors and offerings. This Magic Quadrant will help data and analytics leaders make the right choices in a complex and fast-evolving market. Strategic Planning Assumptions By 2025, cloud preference for data management will substantially reduce the vendor landscape while the growth in multicloud will increase the complexity for data governance and integration. By 2022, cloud database management system (DBMS) revenue will account for 50% of the total DBMS market revenue. These DBMSs reflect optimization strategies designed to support transactions and/or analytical processing for one or more of the following use cases:     Traditional and augmented transaction processing     Traditional and logical data warehouse     Data science exploration/deep learning     Stream/event processing   ...

  The following post focuses on AWS, MS Azure, and GCP in detail. Learn more about each cloud service and how to choose the best one for your business needs.  Digitalization is being embraced by all of us across the globe, especially cloud computing technology. Whether it's because of its scalability or security or reduced costs, cloud platforms have sprung up to a great extent over a few years. Gone are the days when businesses were confused about whether to choose a cloud service provider or not. Now the confusion surrounds the question of which cloud service provider to use. AWS, Azure, and Google Cloud are our top three contenders. Recently, I happen to stumble upon an informative post focusing on AWS Lambda vs Azure Functions. I must say this one was quite detailed and well-structured. Here they have successfully covered all the aspects that are essential and dominating while we compare lambda vs azure. And I am pretty sure considering both the posts together will act a...

As an industry, we’ve gotten exceptionally good at building large, complex software systems. We’re now starting to see the rise of massive, complex systems built around data – where the primary business value of the system comes from the analysis of data, rather than the software directly. We’re seeing quick-moving impacts of this trend across the industry, including the emergence of new roles, shifts in customer spending, and the emergence of new startups providing infrastructure and tooling around data. In fact, many of today’s fastest growing infrastructure startups build products to manage data. These systems enable data-driven decision making (analytic systems) and drive data-powered products, including with machine learning (operational systems). They range from the pipes that carry data, to storage solutions that house data, to SQL engines that analyze data, to dashboards that make data easy to understand – from data science and machine learning libraries, to automated data pipe...

What Is Mainframe Modernization? Mainframe Modernization entails the process of migrating or improving the IT operations to reduce IT spending efficiently. In the realm of improving, we can define Mainframe Modernization as the process of enhancing legacy infrastructure by incorporating modern interfaces, code modernization, and performance modernization. In terms of migration, it is the process of shifting the enterprise’s code and functionality to a newer platform technology like cloud systems. The strategy employed to modernize Mainframe structures relies on factors like business/customer objectives, IT budgets, and costs of running new technology vs. costs incurred from not modernizing. B enefits of Mainframe Modernization to Cloud Cloud can offer economies of scale and new functions that are not available through mainframe computing. The benefits of cloud technologies and the law of diminishing returns in the Mainframe are calling for an increased demand for migration strate...