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
Building Real-Time Data Governance at Scale with Apache Kafka ft. Tushar Thole
Handling 2 Million Apache Kafka Messages Per Second at Honeycomb
Why Data Mesh? ft. Ben Stopford
Serverless Stream Processing with Apache Kafka ft. Bill Bejeck
The Evolution of Apache Kafka: From In-House Infrastructure to Managed Cloud Service ft. Jay Kreps
What’s Next for the Streaming Audio Podcast ft. Kris Jenkins
On to the Next Chapter ft. Tim Berglund
Intro to Event Sourcing with Apache Kafka ft. Anna McDonald
Expanding Apache Kafka Multi-Tenancy for Cloud-Native Systems ft. Anna Povzner and Anastasia Vela
Apache Kafka 3.1 - Overview of Latest Features, Updates, and KIPs
Optimizing Cloud-Native Apache Kafka Performance ft. Alok Nikhil and Adithya Chandra
From Batch to Real-Time: Tips for Streaming Data Pipelines with Apache Kafka ft. Danica Fine
Real-Time Change Data Capture and Data Integration with Apache Kafka and Qlik
Modernizing Banking Architectures with Apache Kafka ft. Fotios Filacouris
Running Hundreds of Stream Processing Applications with Apache Kafka at Wise
Lessons Learned From Designing Serverless Apache Kafka ft. Prachetaa Raghavan
Using Apache Kafka as Cloud-Native Data System ft. Gwen Shapira
ksqlDB Fundamentals: How Apache Kafka, SQL, and ksqlDB Work Together ft. Simon Aubury
Explaining Stream Processing and Apache Kafka ft. Eugene Meidinger
Handling Message Errors and Dead Letter Queues in Apache Kafka ft. Jason Bell
Create your
podcast in
minutes
It is Free
Insight Story: Tech Trends Unpacked
Zero-Shot
Fast Forward by Tomorrow Unlocked: Tech past, tech future
The Unbelivable Truth - Series 1 - 26 including specials and pilot
Lex Fridman Podcast