From the Founder and CEO of GridGain Systems

Nikita Ivanov

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Top Stories by Nikita Ivanov

A few months ago, I spoke at the conference where I explained the difference between caching and an in-memory data grid. Today, having realized that many people are also looking to better understand the difference between two major categories in in-memory computing: In-Memory Database and In-Memory Data Grid, I am sharing the succinct version of my thinking on this topic - thanks to a recent analyst call that helped to put everything in place  TL;DR Skip to conclusion to get the bottom line. Nomenclature Let's clarify the naming and buzzwords first. In-Memory Database (IMDB) is a well-established category name and it is typically used unambiguously. It is important to note that there is a new crop of traditional databases with serious In-Memory "options". That includes MS SQL 2014, Oracle's Exalytics and Exadata, and IBM DB2 with BLU offerings. The line is blurry be... (more)

A Mature In-Memory Data Fabric By @GridGain | @CloudExpo [#BigData]

Why the Fast Data World Needs a Proven and Mature In-Memory Data Fabric Much of what human beings experience as commonplace today - social networking, online gaming, mobile and wearable computing -- was impossible a decade ago. One thing is certain: we're going to see even more impressive advances in the next few years. However, this will be the result of a fundamental change in computing, as current methods have reached their limit in terms of speed and volume. Traditional disk-based storage infrastructure is far too slow to meet today's data demands for speed at volume, which ar... (more)

Columnar vs. Key-Value Storage Models

What are the performance differences between in-memory columnar databases like SAP HANA and GridGain's In-Memory Database (IMDB) utilizing distributed key-value storage? This questions comes up regularly in conversations with our customers and the answer is not very obvious. Storage Models First off, let's clearly state that we are talking about storage model only and its implications on performance for various use cases. It's important to note that: Storage model doesn't dictate of preclude a particular transactionality or consistency guarantees; there are columnar databases tha... (more)

Hadoop – 100x Faster By @GridGain | @CloudExpo [#BigData]

If you know anything about Hadoop architecture - the task seemed daunting to us and it proved to be one of the most challenging engineering feat that we have accomplished so far. After almost 24 months of development, tens of thousands of lines of Java, Scala and C++ code, multiple design iterations, several releases and dozens of benchmarks later we have the product that can deliver real-time performance to Hadoop with only minimal integration and no ETL required. Backed-up by customer deployments that prove our performance claims and validate our architecture. Here's how we d... (more)

In-Memory Computing: In Plain English

After five days (and eleven meetings) with new customers in Europe, Russia, and the Middle East, I think time is right for another refinement of in-memory computing's definition. To me, it is clear that our industry is lagging when it comes to explaining in-memory computing to potential customers and defining what in-memory computing is really about. We struggle to come up with a simple, understandable definition of what in-memory computing is all about, what problems it solves, and what uses are a good fit for the technology. In-Memory Computing: What Is It? In-memory computin... (more)