In Memory Big Data Heats Up With Apache Ignite

(Excerpt from original post on the Taneja Group News Blog)

Recently we posted about GridGain contributing their core in-memory solution to the Apache Ignite project. While this is still incubating, it’s clear that this was a good move for GridGrain, and a win for the big data/BI community in general. Today Apache Ignite drops its v1.0 release candidate with some new features added in like built-in support for jCache and an autoloader to help migrate data and schema in from existing SQL databases (e.g. Oracle, MySQL, Postgres, DB2, Microsoft SQL, etc.).

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IoT Goes Real-Time, Gets Predictive – Glassbeam Launches Spark-based Machine Learning

(Excerpt from original post on the Taneja Group News Blog)

In-Memory processing was all the rage at Strata 2014 NY last month, and the hottest word was Spark! Spark is big data scale-out cluster solution that provides a way to speedily analyze large data sets in-memory using a “resilient distributed data” design for fault-tolerance.  It can deploy into its own optimized cluster, or ride on top of Hadoop 2.0 using YARN, (although it is a different processing platform/paradigm from MapReduce – see this post on GridGain for a Hadoop MR In-memory solution).

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GridGain Turns Over In-Memory Platform To Apache As Ignite Project

(Excerpt from original post on the Taneja Group News Blog)

Recently I wrapped up a report on GridGain’s In Memory Hadoop Accelerator in which I explored how leveraging memory can vastly improve the production performance of many Hadoop MapReduce jobs, and even tackle streaming use cases without re-writing them or implementing newer streaming paradigms. GridGain drops into existing Hadoop environments without much fuss, so it’s an easy add-on/upgrade. Now GridGain has just transferred the core in-memory platform over to Apache Software Foundation as the newly accepted Apache incubator Ignite project, completely contributed to the community at large.

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