MapR Technologies at the Strata Data Conference this week advanced its case of processing analytics in real time via an update to the database it makes available to run on the converged system it developed on top of Hadoop.
The latest version of MapR-DB adds support for self-service SQL data exploration as well as connectors to the Apache Spark in-memory computing framework and Apache Hive data warehouse software.
Jack Norris, senior vice president for data and applications at MapR, says the company is committed to supporting a broad range of data types and models on top of MapR-DB. To that end, the new release also adds support for the Open JSON Application Interface (OJAI) 2.0 specification that makes it possible to access structured, unstructured, and semi-structured data via a common application programming interface.
Other new capabilities include support for secondary indexes, smart query executions, and real-time application integration that includes global data capture.https://o1.qnsr.com/log/p.gif?;n=203;c=204663295;s=11915;x=7936;f=201904081034270;u=j;z=TIMESTAMP;a=20410779;e=i
Norris says that as IT continues to evolve, it’s clear now that just about every operational process is going to need to be infused with analytics in real time to optimize it. “Otherwise, the opportunity to respond to events as they occur is lost,” says Norris.
“Applications need to be able to automatically access analytics as part of the business process,” says Norris.
As apparent as that may seem, most analytics today is applied to processes retroactively. While there’s often value in that approach, when most business leaders discuss digital business transformation they are really talking about analytics in real time to either improve a customer experience or maximize utilization of a limited resource. The challenge facing IT organizations now is figuring out how best to go about making the real-time analytics required to achieve those goals as broadly available as possible.