RainStor announced today that RainStor's Big Data Analytics on Hadoop product has been validated with the Intel Distribution for Apache Hadoop software.
Hadoop is becoming increasingly attractive as a platform for large-scale, lower cost data management and analysis. Using RainStor in combination with the Intel Distribution is designed to lower the TCO by reducing storage and at the same time improve the query and analytics performance using standard SQL access and MapReduce, Pig, Hive and more.
RainStor reported the following features of its Big Data Analytics on Hadoop solution:
- Extreme Data Load: Handling billions of records per day. As the data is being loaded at network speed, it is automatically compressed.
- High Data Compression: 20-40X yielding 95+ percent reduction in the data footprint.
- Speed of Query & Analysis: Search and analyze data using native SQL, as well as MapReduce for integration with existing systems and tools, etabling analysis on Hadoop without the need to move data out to a data warehouse.
- Predictable Scale: RainStor's ability to reduce the storage and nodes across the Hadoop cluster gives customers cost savings and ease of scale to meet future requirements.
Applicable use-cases and solutions include:
- Analytics Data Hub: With RainStor, customers can collect and store volumes of data generated at network. Examples include communications, network, clickstream, logs and financial data for ongoing query and analysis at significantly lower cost compared to traditional database and data warehouse technologies.
- Online Data Archive: Customers can move historical data into RainStor from any data warehouse, freeing up the data warehouse to perform analysis against the most current data, while continuously providing on-demand access to the historical set in RainStor.