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Thomas Krafft

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Top Stories by Thomas Krafft

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 management and monitoring capabilities now available with all products, and out-of-box integration with virtually any existing data source, Hadoop-based systems, and Java, C++, .NET, Android and iOS applications. Specifically related to GridGain’s new products, the company now offers three w... (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)

GridGain 4.0 Released!

I’m pleased to announce that today we released GridGain 4.0 – latest edition of our platform for Real Time Big Data processing. I’m proud that our team set this final deadline almost 5 months ago and we were able to hit without a single delay. I’m especially proud of this fact because of the enormous complexity of the development process involved in making software like GridGain – dozens of production clients, testing on serious massively distributed environments, set of new features, and the usual array of setbacks that we had to go through to get here. Needless to say that w... (more)

Adding LevelDB Store For Your In-Memory Cache?

Recently, at one of the customer meetings, I was asked whether GridGain comes with its own database. Naturally my reaction was – why? GridGain easily integrates pretty much with any persistent store you wish, including any RDBMS, NoSql, or HDFS stores. However, then I thought, why not? We already have cache swap space (disk overflow) storage based on Google LevelDB key-value database implementation, so why not have the same for data store. Here is how easy it was to add LevelDB based data store implementation for GridGain cache – literally took me 20 minutes to do, including unit ... (more)

In-Memory Processing as a Business Use Case

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 d... (more)