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DataStax Manages Big Data Trio: Real-Time, Analytic, and Search

DataStax is pushing forward its commercial offerings for the Apache Cassandra open source distributed database management system with this week's release of its DataStax Enterprise 2.0 product.

Aiming to combine and manage a triumvirate of "real-time", "analytic", and now "enterprise search" data all in the same database cluster. The DSE 2.0 platform manages real-time data with Cassandra, provides batch analytic capabilities with Apache Hadoop, and enables enterprise search on that same data with Apache Solr.

"Enterprise search is a high-demand capability and open-source Apache Solr is the most popular search technology available," said Billy Bosworth, CEO of DataStax. "Solr downloads outnumber even Hadoop downloads today. By combining enterprise search (Solr) with real-time analytic power in DSE 2.0, we're allowing our customers to move faster in response to never-ending user demands by allowing them to focus on building the application instead of battling big data infrastructure."

The aim here is to combine the search capabilities of Solr with the real-time and analytic database capabilities of Cassandra and Hadoop to produce "Google-like capabilities" for a database; but without the data movement routines, which could potentially be both time-consuming and error-prone.

As DataStax Enterprise 2.0 is built on Cassandra, its real-time search operations across multiple data centers also work with automatic "data sharding" and easy index rebuild operations, none of which are found in native Solr. Lastly, developers can access Solr/search information using the SQL-like Cassandra Query Language (CQL), which should make it easier to do real-time operations on search data.

NOTE: Database tools company CodeFutures Corporation provides the following definition for data sharding: "Database sharding can be simply defined as a 'shared-nothing' partitioning scheme for large databases across a number of servers, enabling new levels of database performance and scalability achievable. If you think of broken glass, you can get the concept of sharding — breaking your database down into smaller chunks called shards and spreading those across a number of distributed servers."

With DataStax Enterprise 2.0, users can run real-time, analytic, and search operations in the same database cluster without any of the workloads affecting the other from a performance or resource contention standpoint. The platform does not require ETL software to move data between systems, because everything is automatically and transparently replicated in the cluster, even if that cluster spans multiple data centers or is implemented in the cloud.

"With DataStax Enterprise, DataStax introduced the ability to use the same database cluster to support operational and analytic workloads. With version 2.0 the company has taken that one step further with the integration of enterprise search and elastic workload re-provisioning," said Matt Aslett, research manager, data management and analytics, 451 Research. “The new capabilities expand the way companies are able to use and analyze their data, avoiding the need to host redundant systems that have to be kept in-synch."

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