Modernizing the Monolith with Moti Rafalin and Amir Rapson

Java Applications became the go-to preference of most developers because of the write-once-run-anywhere advantage it gave over other languages. And it didn’t take much time for Java to become the language for the enterprise. Consequently, most enterprises are still running legacy Java Monoliths on their infrastructure.

Breaking up a monolith is not an easy process–nor is it something that every company should do just because they have a monolith. In some cases, a monolith is just fine. But sometimes, you do need to decompose a monolith as the complexity of the monolith grows and leads to longer release cycles or scalability issues. Breaking down into microservices is a natural way to shift legacy applications to the cloud.

vFunction is an artificial intelligence platform that assesses, analyzes, helps you design microservices and then automatically creates those microservices for you. So it’s an end-to-end platform from analysis to the actual creation of the code of those microservices with their respective APIs. While vFunction started with Java, they are expanding these capabilities to other platforms as well. Moti Rafalin who is the CEO of vFunction and Amir Rapson who is the CTO of vFunction join us today.

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This episode is supported by AWS Insiders. AWS Insiders is an edgy, entertaining podcast about the services and future of cloud computing at AWS. In every episode, hosts Rahul Subramaniam and Hilary Doyle invite a top guest from the world of cloud computing to debate the most contentious topics in the industry —  from the future of serverless to SQL versus noSQL , and from multi-cloud to locking in on a single cloud vendor. We think you should check it out! Search for AWS Insiders in your podcast player. Click on to get in on the fun.

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There’s a lot of work required to bring a project from whiteboard to development, and none of us can do it alone.

The host, Burr Sutter, is a Red Hatter and lifelong developer advocate and community organizer. I checked out the deep learning episode of Code Comments, and I really appreciated Burr’s guidance through the conversation. The episode tackles a deep but fascinating topic. It covers use cases, realistic examples, and motivations for the relevant technology. I think their approach allows for easier contextualization of these significant topics in the coding space.

Search for Code Comments in your podcast player! You can check them out by clicking the link:


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