Manage & Scale: Big Data Management at Massive Scale

As incoming data volumes continue to rise, the infrastructure requires a constant balancing act between Big Data management and scale. The introduction of compliance requirements adds to the complexity of this issue. RainStor is purpose-built to scale smoothly to meet data-loading priorities and accommodate future growth.

As volumes grow to petabyte scale, RainStor relies on massive compression and de-duplication to do far more with less. It’s essentially a hardware multiplier that uses less storage space, CPU and network I/O operations, while still enabling platform extensions for growth.

It’s also easy to install—an end-user can be trained with our Big Data management technology in a week or less. And since RainStor works with standard SQL Query, no DBA specialists are needed to run and manage the product. That’s another boost to the TCO.

RainStor is hyper-efficient at loading thousands of records per second; it splits the file into chunks of, say, 1 million records, which are then processed in parallel and streamed across a number of CPUs or servers at the same time. This ultimately provides data load rates typically 10X faster than a traditional R-DBMS.

Simplified Administration

RainStor’s ‘lock-and-leave’ approach makes it simple to manage and easy to maintain. Without needing to design indexation strategies, manage table space, set hundreds of parameters appropriately and balance multiple priorities, RainStor dramatically reduces operational costs when compared to similar OLTP and OLAP deployments.
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Extreme Scalability

RainStor provides unique scale in terms of both data volume loading rates but more importantly scale as volumes grow over time to accommodate future growth rates which is especially important as compliance regulations become more stringent.

As data volumes grow to petabyte scale, RainStor’s Big Data management solution relies on its massive compression and de-duplication to do far more with less and essentially use this as a hardware multiplier utilizing smaller amount of storage, less CPU and less network I/O operations and whereby the underlying hardware and storage platforms can be easily extended to meet additional demand.

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