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Portions of this article were taken from the book  High-Performance In-Memory Computing With Apache Ignite . If it got you interested, check out the rest of the book for more helpful information. Before reading, be sure to check out  Part 1 ! Apache Ignite provides a vanilla distributed in-memory file system called Ignite File System (IGFS) with similar functionality to Hadoop HDFS. This is one of the unique features of Apache Ignite that helps accelerate Big Data computing. IGFS implements the Hadoop file system API and is designed to support Hadoop v1 and Yarn Hadoop v2. Ignite IGFS can transparently plug into Hadoop or Spark deployment. One of the greatest benefits of the IGFS is that it does away with Hadoop NamedNode in the Hadoop deployment; it seamlessly utilizes Ignite’s in-memory database under the hood to provide completely automatic scaling and failover without any additional shared storage. IGFS uses memory instead of disk to produce a distributed, fault-tole...

Recently Apache Ignite 2.0 introduce a beta version of the in-memory machine learning grid, which is a distributed machine learning library built on top of the Apache IMDG. This beta release of ML library can perform local and distributed vector, decompositions and matrix algebra operations. The data structure can be stored in Java heap, off-heap or distributed Ignite caches. At this moment, the Apache Ignite ML grid doesn't support any prediction or recommendation analysis. In this short post, we are going to download the new Apache Ignite 2.0 release, build the example and run them. 1. Download and unpack the Apache Ignite 2.0 distribution. Download the Apache Ignite 2.0 binary release version from the following link . Unpack the distribution somewhere in your workstation (e.g /home/ignite/2.0 ) and set the IGNITE_HOME path to the directory. 2. Start the Apache Ignite remote node Run the following command in the terminal window. ignite.sh examples/config/example-ignite....