Why We Added In-Memory to Cassandra

DataStax Enterprise (DSE) 4.0 introduces an in-memory option for Cassandra, offering benefits such as multi-data center/cloud support and flexible data model while maintaining no single point of failure. This new feature allows users to assign data based on performance needs, including spinning disk for lesser workloads, SSDs for hotter data, and in-memory for the fastest response times within a single database cluster. DSE is one of the few NoSQL databases that can handle both big data scale and in-memory use cases, making it suitable for applications like eBay with massive big data workloads alongside low latency requirements. In-memory computing has become popular among IT staffs aiming to create fast online apps, but traditional relational database management systems (RDBMS) still suffer from rigid data models, multiple points of failure, and inability to truly scale out linearly. Cassandra with in-memory computing addresses these issues, offering continuous availability, linear scale performance, support for modern data types, and multi-data center/cloud availability that legacy RDBMS technology cannot match.