If you’ve heard the term “clusters,” then you might know it refers to Confluent components and features that we run in all three major cloud providers today, including an event streaming platform based on Apache Kafka®, ksqlDB, Kafka Connect, the Kafka API, databalancers, and Kafka API services. Rashmi Prabhu, a software engineer on the Control Plane team at Confluent, has the opportunity to help govern the data plane that comprises all these clusters and enables API-driven operations on these clusters.
But running operations on the cloud in a scaling organization can be time consuming, error prone, and tedious. This episode addresses manual upgrades and rolling restarts of Confluent Cloud clusters during releases, fixes, experiments, and the like, and more importantly, the progress that’s been made to switch from manual operations to an almost fully automated process. You’ll get a sneak peek into what upcoming plans to make cluster operations a fully automated process using the Cluster Upgrader, a new microservice in Java built with Vertx. This service runs as part of the control plane and exposes an API to the user to submit their workflows and target a set of clusters. It performs statement management on the workflow in the backend using Postgres.
So what’s next? Looking forward, there will be the selection phase will be improved to support policy-based deployment strategies that enable you to plan ahead and choose how you want to phase your deployments (e.g., first Azure followed by part of Amazon Web Services and then Google Cloud, or maybe Confluent internal clusters on all cloud providers followed by customer clusters on Google Cloud, Azure, and finally AWS)—the possibilities are endless!
The process will become more flexible, more configurable, and more error tolerant so that you can take measured risks and experience a standardized way of operating Cloud. In addition, expanding operation automations to internal application deployments and other kinds of fleet management operations that fit the “Select/Apply/Monitor” paradigm are in the works.
EPISODE LINKS
Confluent Platform 7.0: New Features + Updates
Real-Time Stream Processing with Kafka Streams ft. Bill Bejeck
Automating Infrastructure as Code with Apache Kafka and Confluent ft. Rosemary Wang
Getting Started with Spring for Apache Kafka ft. Viktor Gamov
Powering Event-Driven Architectures on Microsoft Azure with Confluent
Automating DevOps for Apache Kafka and Confluent ft. Pere Urbón-Bayes
Intro to Kafka Connect: Core Components and Architecture ft. Robin Moffatt
Designing a Cluster Rollout Management System for Apache Kafka ft. Twesha Modi
Apache Kafka 3.0 - Improving KRaft and an Overview of New Features
How to Build a Strong Developer Community with Global Engagement ft. Robin Moffatt and Ale Murray
What Is Data Mesh, and How Does it Work? ft. Zhamak Dehghani
Multi-Cluster Apache Kafka with Cluster Linking ft. Nikhil Bhatia
Using Apache Kafka and ksqlDB for Data Replication at Bolt
Placing Apache Kafka at the Heart of a Data Revolution at Saxo Bank
Advanced Stream Processing with ksqlDB ft. Michael Drogalis
Minimizing Software Speciation with ksqlDB and Kafka Streams ft. Mitch Seymour
Collecting Data with a Custom SIEM System Built on Apache Kafka and Kafka Connect ft. Vitalii Rudenskyi
Consistent, Complete Distributed Stream Processing ft. Guozhang Wang
Powering Real-Time Analytics with Apache Kafka and Rockset
Automated Event-Driven Architectures and Microservices with Apache Kafka and SmartBear
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