RainStor has announced Big Data Analytics on Hadoop, which it describes as the industry's first enterprise-class database that runs natively on Hadoop.
The online database repository provider's Big Data Analytics on Hadoop software enables faster analytics processing because there's no need to to move data out of the Hadoop Distributed File System (HDFS) environment.
Hadoop is an open source aggregator of structured and unstructured data that allows huge volumes of information to be analysed, including online transactions, social media data and web logs.
RainStor provides a specialised database purpose-built for online big data retention.
By running the database natively on Hadoop, RainStor said it can produce faster query and analysis against multi-structured data inside Hadoop. For example, neither Oracle nor SQL databases run natively on Hadoop or HDFS, so they require that data be analysed using outside sources.
RainStor can also analyse compressed data in its native state.
RainStor's new Big Data Analytics on Hadoop combines a compression algorithm with around 40:1 data reduction along with providing SQL and Oracle access and MapReduce. The compressed data set, both structured and unstructured, running on HDFS reduces the cluster size by 50% to 80%, which significantly lowers operating cost, according to RainStor CEO John Bantleman.
"We do partition filtering. Our index says, 'go find me this record,' and filtering asks 'does this value exist in a partition? Yes or no,'" Bantleman said. "So when you ask a question, we can look at a tiny slice of metadata and decide quickly what not to read, so it does a lot less work than big databases do."