Everything we do generates events – click on a mobile ad, pay with a credit card, tweet, measure heart rate, accelerate on the gas pedal, etc. What if an organization can feed these events into predictive models as soon as the event happens to quickly and more accurately make decisions that generate more revenue, lower costs, minimize risk, and improve the quality of care? You would need deep and fast analytics provided by Big Data platforms such as Pivotal HD 2.0 announced yesterday.
Pivotal HD 2.0 brings an in-memory, SQL database to Hadoop through seamless integration with Pivotal GemFire XD, enabling you to combine real-time data with historical data managed in HDFS. Closed loop analytics, operational BI, and high-speed data ingest are now possible in a single OLTP/OLAP platform without any ETL processing required. Use cases are ones that are time sensitive in nature. For example, telecom companies are at the forefront of applying real-time Big Data analytics to network traffic. The “store first, analyze second” method does not make sense for rapidly shifting traffic that requires immediate action when issues arise.
I spoke with Senior Director of Engineering at Pivotal Makarand Gokhale to explain the value in bringing OLTP to a traditional batch processing Hadoop.
1. Real-time solutions for Hadoop can mean many things- performing interactive queries, real-time event processing, and fast data ingest. How would you describe Pivotal HD’s real-time data services for Hadoop?