Oracle Vs Cassandra

 

Oracle Coherence is a pure in-memory cache which can be distributed across nodes. Depending on its configuration it can have strong consistency, or eventual consistency for inserts and updates. Coherence is object based - consistent data model. Since you buy Coherence from oracle - you can get commercial support, from oracle.

An Oracle database is a collection of data treated as a unit. The purpose of a database is to store and retrieve related information. A database server is the key to solving the problems of information management. In general, a server reliably manages a large amount of data in a multi- user environment so that many users can concurrently access the same data. All this is accomplished while delivering high performance. A database server also prevents unauthorized access and provides efficient solutions for failure recovery.

Oracle Database is the first database designed for enterprise grid computing, the most flexible and cost effective way to manage information and applications. Enterprise grid computing creates large pools of industry-standard, modular storage and servers. With this architecture, each new system can be rapidly provisioned from the pool of components. There is no need for peak workloads, because capacity can be easily added or reallocated from the resource pools as needed.

The database has logical structures and physical structures. Because the physical and logical structures are separate, the physical storage of data can be managed without affecting the access to logical storage structures.

Cassandra  is one of the hottest of the NoSQL databases.  From a production DBAs perspective it’s not hard to see why:  while some of the other NoSQLs offer more programming bells and whistles for the developer, Cassandra is built from the ground up for total and transparency redundancy and scalability, close to the heart of every DBA.

However,  Cassandra involves some complex data modelling concepts – mainly around the notorious Super Column concept .

Cassandra is a big table data store that is distributed across nodes. No single point of failure. It uses some caching to improve performance before committing the data to disk in its implementation of bigTable. Cassandra requires some structure in its tuple (key/value/timestamp) but otherwise can support flexible data structures. 

Preferences should be determined by your use case. They are both pretty cool in their own right. 

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 data model offers the convenience of column indexes with the performance of log-structured updates, strong support for denormalization and materialized views, and powerful built-in caching.