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Thomas Krafft

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Top Stories by Thomas Krafft

GridGain Closes $2.5 Million Series A Funding to Accelerate Innovation in Real Time Big Data Processing Innovative Cloud-Based Software Middleware Provider Receives Financing Led by RTP Ventures Foster City, CA (PRWEB) December 06, 2011 — GridGain, the leader in high performance cloud computing and real time big data processing, today announced that it has closed a $2.5 million Series A round of financing led by RTP Ventures. The company will use the new funding to accelerate growth, continue innovation in real time big data processing, and expand its global market share. News Facts GridGain is used by leading Fortune 500 companies including Sony, Apple, McGrawHill, Avis, TomTom, Markit, Daiwa, Thomson Reuters, and others across the finance, retail, Web 2.0 and telecom markets More than 500 businesses use GridGain’s software on a daily basis to address the scalab... (more)

Are Hadoop's Days Numbered?

Interesting article at GigaOm: http://bit.ly/OINpfr I won’t repeat the main points – but basically it says that since Hadoop is disk/ETL/batch based it won’t fit for real time processing of frequently changing data. Author correctly points out that real time processing (i.e. perceptual real time meaning sub-second to few seconds response time) is becoming a HUGE trend that’s impossible to ignore. He points to Google that moved away from Hadoop MapReduce-like approach towards massively distributed in-memory platform for its various projects like Precolator and Dremel… So, What’s ... (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)

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)