Having understood the strategic significance of integrated and insightful applications, enterprises are strategizing and planning modernization and migration plans. As reported earlier, most of the enterprise, cloud, web, and embedded applications are being built as containerized and microservices-centric applications; legacy applications should be modified with a variety of automation processes and products.

Blu Age Velocity (https://www.bluage.com/products/blu-age-velocity) is famous for automated modernization. This offering automates and accelerates modernizing legacy applications. It can do both reverse and forward engineering. That is, it can translate legacy applications into microservices-centric applications. Readers can find many case studies and references for automated application modernization at https://www.bluage.com/references.

We all know that there are two key data-processing methods. Batch or bulk processing is all about batching or accumulating data to initiate the processing at scheduled hours. However, with the availability of real-time data-processing technologies and platforms, real-time data processing is picking up. Also, data starts to lose its value as time passes, and hence it should be collected, cleansed, and crunched immediately in order to extricate timely insights. In the mainframe era, batch processing was the main method for data processing due to inherent IT resource constraints. Legacy applications are predominantly single-threaded and hence parallel execution isn't possible at the language level. With multi-threaded languages and applications, parallel execution gains immense prominence. With multi-core and multi-processor computers becoming affordable, parallel processing at the infrastructure level is being achieved. With the emergence of virtual machines and containers, having multiple instances of these server resources leads us to fast-track the application's execution. Newer programming languages intrinsically support multi-threading and hence concurrent processing is prominent these days. With cloud infrastructures increasingly being compartmentalized, the goal of doing tasks in parallel has gained momentum. Now, with the surge of edge, local, and remote cloud environments, these restrictions are slowly fading away and real-time analytics is booming. That is, legacy applications that previously did batch processing are modernized to do real-time processing using cloud-based platforms.