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
Event-Driven Systems and Agile Operations
Streaming Analytics and Real-Time Signal Processing with Apache Kafka
Blockchain Data Integration with Apache Kafka
Automating Multi-Cloud Apache Kafka Cluster Rollouts
Common Apache Kafka Mistakes to Avoid
Tips For Writing Abstracts and Speaking at Conferences
How I Became a Developer Advocate
Data Mesh Architecture: A Modern Distributed Data Model
Flink vs Kafka Streams/ksqlDB: Comparing Stream Processing Tools
Practical Data Pipeline: Build a Plant Monitoring System with ksqlDB
Apache Kafka 3.2 - New Features & Improvements
Scaling Apache Kafka Clusters on Confluent Cloud ft. Ajit Yagaty and Aashish Kohli
Streaming Analytics on 50M Events Per Day with Confluent Cloud at Picnic
Build a Data Streaming App with Apache Kafka and JS - Coding in Motion
Optimizing Apache Kafka's Internals with Its Co-Creator Jun Rao
Using Event-Driven Design with Apache Kafka Streaming Applications ft. Bobby Calderwood
Monitoring Extreme-Scale Apache Kafka Using eBPF at New Relic
Confluent Platform 7.1: New Features + Updates
Scaling an Apache Kafka Based Architecture at Therapie Clinic
Bridging Frontend and Backend with GraphQL and Apache Kafka ft. Gerard Klijs
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