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Thomas Krafft

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Top Stories by Thomas Krafft

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 New?! The widespread confusion about Hadoop’s role and its applicability is becoming alarming… Hadoop was never designed to process anything in real time or process live streaming data or process anything that’s rapidly changing. Hadoop’s core is HDFS technology – a highly scalable distribu... (more)

GridGain Gives Customers Even Bigger Opportunities from Big Data

As increasing numbers of industry leading companies look to new technologies to speed their real-time services and Big Data applications, GridGain Systems, leading developer of scalable, distributed in-memory compute and data grid technologies for real-time data processing, is strongly positioned to serve the demand. This week, GridGain announced the release of an entirely new suite of solution-focused products built around the company’s leading in-memory and world’s fastest MapReduce technologies, expanded enterprise consulting and other professional services, advanced DevOps 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)

You’re invited: 7/26 – Live Coding In-Memory Big Data with GridGain

GridGain will be hosting a webinar Thursday, July 26, 2012 at 3pm EST / 12pm PST during which GridGain’s CTO, Dmitriy Setrakyan, will be live coding GridGain examples in Java. Dmitriy will cover the following examples: How to distribute simple units of work to the grid. Collocation of computation and data. A full Streaming MapReduce example that performs SQL queries on streaming in-memory data. This is a fantastic opportunity to see how easy it is to get started with GridGain, so register now and join us for “Live Coding In-memory Bid Data with GridGain.” ... (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)