In this episode of Kongcast, I spoke with Scott Lowe, principal field engineer at Kong, about what a service mesh does and when to use it, among other common mesh-related questions. Check out the transcript and video from our conversation below, and be sure to subscribe to get email alerts for the latest new episodes.

It’s easy to take continuous integration (CI) and continuous delivery/deployment (CD) for granted these days, but these have been transformational concepts that have drastically changed the face of software development over the past thirty years.

While CI/CD is synonymous with modern software development best practices, today’s machine learning (ML) practitioners still lack similar tools and workflows for operating the ML development lifecycle on a level on par with software engineers. For background, follow a brief history of transformational CI/CD concepts and how they’re missing from today’s ML development lifecycle.

Coming off of our Snowday event, we’ve unveiled a number of new product capabilities that expand what is possible in the Data Cloud. From helping businesses operate globally with improved replication efficiency, empowering developers with new functionality in Snowpark, and improving the security and governance of data through native object tagging, there is no shortage of exciting advancements coming to Snowflake.

REST and (the newer) GraphQL APIs are the core technologies behind the vast most of today’s integrations. These APIs allow external developers to tap into the functionality of the major platforms and build in their custom functionality to suit their needs. The fundamental difference is that REST is an architectural design framework based on HTTP, while GraphQL is a query syntax that is not transport-dependent.