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Thomas Krafft

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Top Stories by Thomas Krafft

GridGain will be presenting at QCon London 2012 in London on March 7-9. This is going to be new presentation that we’ve specifically prepared: Live Scala coding of streaming real time MapReduce application… We are going to have Hadoop’s ubiquitous popular word counting example and turn it on its head making it a real time MapReduce application using upcoming GridGain 4.0. Come to see at our booth and talk to our CTO Dmitriy Setrakyan who’s be coding GridGain software since 2005. Hope to see as many of you as possible! ... (more)

“Modern HPC with GridGain & Scala” at NYC Scala Meetup

Tuesday, June 5, 2012 6:30 PM Meetup HQ – 9th Floor 632 Broadway Suite 901, New York, NY Dmitriy Setrakyan, CTO at GridGain Systems, will present at NY Scala Meetup. Dmitriy Setrakyan will talk about using GridGain for highly distributed HPC programming in Scala. As one of the main example we will be walking through a realtime word counting program with constantly changing text and will compare it with a Hadoop word counting example. You will see some cool features of GridGain such as Auto-Discovery, Streaming MapReaduce, Zero Deployment, Distributed Data Partitioning, and In-M... (more)

GridGain 4.2 Released!

We are happy to announce that GridGain 4.2 is released! This release includes several new exciting feature as well as the host of performance optimizations that we’ve included. This release is 100% backward compatible with 4.x product line and we recommend anyone on 4.x version to update as soon as possible. Now – let’s talk about new features… Delayed Preloading In GridGain 4.2 we’ve introduced support for delayed preloading. Dmitriy Setrakyan wrote an excellent blog detailing this new functionality. Essentially, whenever a new node joins the grid or an existing node leaves th... (more)

Berkeley Researchers Highlight Emergence of In-Memory Processing

Excellent paper released by researchers at University of California, Berkeley . They have analyzed data from Hadoop installation at Facebook (one of the largest as such in the world) looking at various metrics for Hadoop jobs running at Facebook datacenter that has over 3,000 computers dedicated to Hadoop-based processing. They have come up with very interesting insights. I advise everyone read it firsthand but I will list some of the interesting bits. Traditional quest for disk locality (a.k.a. affinity between the Hadoop task and the disk that contains the input data for that ... (more)

GridGain and Hadoop: Differences and Synergies

GridGain is Java-based middleware for in-memory processing of big data in a distributed environment. It is based on high performance in-memory data platform that integrates fast In-Memory MapReduce implementation with In-Memory Data Grid technology delivering easy to use and easy to scale software. Using GridGain you can process terabytes of data, on 1000s of nodes in under a second. GridGain typically resides between business, analytics, transactional or BI applications and long term data storage such as RDBMS, ERP or Hadoop HDFS, and provides in-memory data platform for high p... (more)