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
Real-time Threat Detection Using Machine Learning and Apache Kafka
Improving Apache Kafka Scalability and Elasticity with Tiered Storage
Decoupling with Event-Driven Architecture
If Streaming Is the Answer, Why Are We Still Doing Batch?
Security for Real-Time Data Stream Processing with Confluent Cloud
Running Apache Kafka in Production
Build a Real Time AI Data Platform with Apache Kafka
Optimizing Apache JVMs for Apache Kafka
Apache Kafka 3.3 - KRaft, Kafka Core, Streams, & Connect Updates
Application Data Streaming with Apache Kafka and Swim
International Podcast Day - Apache Kafka Edition | Streaming Audio Special
How to Build a Reactive Event Streaming App - Coding in Motion
Real-Time Stream Processing, Monitoring, and Analytics With Apache Kafka
Reddit Sentiment Analysis with Apache Kafka-Based Microservices
Capacity Planning Your Apache Kafka Cluster
Streaming Real-Time Sporting Analytics for World Table Tennis
Real-Time Event Distribution with Data Mesh
Apache Kafka Security Best Practices
What Could Go Wrong with a Kafka JDBC Connector?
Apache Kafka Networking with Confluent Cloud
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