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Thomas Krafft

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Top Stories by Thomas Krafft

In-memory processing is becoming a business necessity in a similar way as collecting and processing ever increasing data sets (a.k.a Big Data) has become a business “must have” rather than just a simple technology in the last five years. Both of these trends are intervened in an interesting ways. Let me explain… 1. Storing Necessitates Processing The initial foray into BigData for many companies was all about storing the data and then some rudimentary processing that most of the time resulted in some trivialized analytics run on log files, purchase history, and similar type of data (that’s what 90% of analytics are still doing today if you ask people on the “inside”). As the amount of data stored kept growing (as well as associated direct and indirect cost) the IT departments were more and more pressured to get deeper and more actionable, i.e. operational, insights ... (more)

Debunking DRAM vs. Flash Controversy vis-a-vis In-Memory Processing

Wikibon produced an interesting material (looks like paid by Aerospike, NoSQL database recently emerged by resurrecting failed CitrusLeaf and acquihiring AlchemyDB, which product, of course, was recommended in the end) that compares NoSQL databases based on storing data in flash-based SSD vs. storing data in DRAM. There are number of factual problems with that paper and I want to point them out. Note that Wikibon doesn’t mention GridGain in this study (we are not a NoSQL datastore per-se after all) so I don’t have any bone in this game other than annoyance with biased and factu... (more)

Micro Cloud in Your JVM: Code Example

Few days ago I blogged about how GridGain easily supports starting many GridGain nodes in the single JVM – which is a huge productivity boost during the development. I’ve got a lot of requests to show the code – so here it is (next page). This is an example that we are shipping with upcoming 4.3 release (entire source code): import org.gridgain.grid.*; import org.gridgain.grid.spi.discovery.tcp.*; import org.gridgain.grid.spi.discovery.tcp.ipfinder.*; import org.gridgain.grid.spi.discovery.tcp.ipfinder.vm.*; import org.gridgain.grid.typedef.*; import javax.swing.*; import java... (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)

Micro Cloud in Your JVM with GridGain

One of the features in GridGain’s In-Memory Data Platform that often goes unspoken for is ability to launch multiple GridGain nodes in the single JVM. Now, as trivial as it sounds… can you start multiple JBoss or WebLogic or Infinisnap or Gigaspaces or Coherence or (gulp) Hadoop 100% independent runtimes in the single JVM? The answer is no. Even for a simple test run you’ll have to start multiple instances on your computer (or on multiple computers), and debug this via remotely connected debugger, different log windows, different configurations, etc. In one word – awkward… Not so... (more)