Cassandra 4.0 Doubles Down on Performance and Reliability!

Congratulations to the Cassandra community for the unprecedented level of collaboration. Take off with the biggest, fastest, and most stable release ever!

Cassandra 4 - Rocket Comparison

What’s the Big Deal?

For Site Reliablity Engineers:

For Site Reliablity Engineers:

Greater stability with more efficient compaction

Incremental repair makes fixes faster and more reliable

Workload replay with full query logging

For Developers:

For Developers:

Scale out cost effectively with 25% to 70% more throughput

Better customer engagements with up to 7x lower latency

Verifiable peak performance with backpressure

For Enterprises:

For Enterprises:

No noisy neighbors with rogue client throttling

Audit logging for expanded compliance use cases

Lower computing TCO with improved throughput

Long term support with Java 11

The Best Open Source Database for Modern Applications

Out of this world performance. Enterprise compliance. Easier to monitor and repair. Check out these great resources that go into much more detail!

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Third-party ecosystem has their eyes on this release and a number of utilities have already added support for Cassandra 4.0. These include the client driver libraries, Spring Boot and Spring Data, Quarkus, the DataStax Kafka Connector and Bulk Loader, The Last Pickle’s Cassandra Reaper tool for managing repairs, Medusa for handling backup and restore, the Spark Cassandra Connector, The Definitive Guide for Apache Cassandra, and the list goes on.

Cassandra 4.0 is finally here! What is it? Why upgrade? What to do next?

Cassandra 4.0

Join Patrick McFadin (VP, Developer Relations at DataStax) as he dives into the many improvements in Cassandra 4.0, and how to plan and execute a successful upgrade.

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The Future of Cloud-Native Database Begins with Apache Cassandra 4.0

Patrick McFaddinPatrick McFaddin | 8-minute read

Just like cloud-native apps, Cassandra is scalable, elastic, and self-healing. As Kubernetes becomes the default cloud operating system, there is demand for running data platforms alongside applications not just “on”, but “in”

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