The Apache Cassandra database is the right choice when you need scalability and high availability without compromising performance. Linear scalability and proven fault-tolerance on commodity hardware or cloud infrastructure make it the perfect platform for mission-critical data. Cassandra's support for replicating across multiple datacenters is best-in-class, providing lower latency for your users and the peace of mind of knowing that you can survive regional outages.
Cassandra's ColumnFamily data model offers the convenience of column indexes with the performance of log-structured updates, strong support for materialized views, and powerful built-in caching.
The latest release is 1.1.6 (Changes)
Cassandra is in use at Netflix, Twitter, Urban Airship, Constant Contact, Reddit, Cisco, OpenX, Digg, CloudKick, Ooyala, and more companies that have large, active data sets. The largest known Cassandra cluster has over 300 TB of data in over 400 machines.
Data is automatically replicated to multiple nodes for fault-tolerance. Replication across multiple data centers is supported. Failed nodes can be replaced with no downtime.
There are no single points of failure. There are no network bottlenecks. Every node in the cluster is identical.
Choose between synchronous or asynchronous replication for each update. Highly available asynchronous operations are optimized with features like Hinted Handoff and Read Repair.
Allows efficient use for many applications beyond simple key/value.
Read and write throughput both increase linearly as new machines are added, with no downtime or interruption to applications.
Cassandra is suitable for applications that can't afford to lose data, even when an entire data center goes down.
Cassandra support contracts and services are available from third parties.
Many of the Cassandra developers and community members hang out in the #cassandra channel on irc.freenode.net.
If you are new to IRC, you can use a web-based client.
Cassandra High Performance Cookbook, by Ed Capriolo. Covers Cassandra 0.8. Also on Amazon.