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Organizations struggle to process ever-growing data volumes while also trying extract the hidden business value as quickly and efficiently as possible. Technologies like Apache Cassandra™ excel at ad-hoc real-time queries while other solutions such as the Apache Hadoop™ stack are built to analyze large amounts of unstructured data. The ideal solution would combine these capabilities to enable instantaneous analytic access to the flood of data most enterprises are capturing today.
In this breakout session, Jonathan Ellis, CTO, DataStax, will discuss Brisk and its promise of low latency and analytics for big data. DataStax’ Brisk is an enhanced open-source Hadoop and Hive distribution that utilizes Cassandra for its core services. Brisk provides integrated Hadoop MapReduce, Hive and job and task tracking capabilities, while providing an HDFS-compatible storage layer powered by Cassandra.
Using this single platform that provides both a low-latency database for “real-time” web-scale applications and the sort of heavy data analysis you get with Hadoop, enterprises can complete the Big Data picture, from low-latency applications through to tools that analyze data – and the ability to use those tools to feed data back into applications. One key benefit of DataStax’ Brisk is the tight feedback loop it allows between a real-time application and the analytics that follow.
By accelerating the time between data creation and analysis with DataStax’ Brisk, users experience greater reliability, simpler deployment and lower total cost of ownership (TCO) compared to traditional Hadoop solutions.
This session is sponsored by DataStax
Jonathan is CTO and co-founder at DataStax (formerly Riptano). Prior to DataStax, Jonathan worked extensively with Apache Cassandra while employed at Racksace. Prior to Rackspace, Jonathan built a multi-petabyte, scalable storage system based on Reed-Solomon encoding for backup provider Mozy. In addition to his work with DataStax, Jonathan is project chair of Apache Cassandra.