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Nikita Ivanov

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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)

Data World Needs a Mature In-Memory Data Fabric By @c64hacker [#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)

In-Memory Technology Will Open the Doors to a Wave of Innovation

by Abe Kleinfeld and Nikita Ivanov Gordon E. Moore's famously predicted tech explosion was prophetic, but it may have hit a snag.  While the number of transistors on integrated circuits has doubled approximately every two years since his 1965 paper, the ability to process and transact on data hasn't. We're now ingesting data faster than we can make sense of it, leaving computing at an impasse. Without a new approach, the innovation promised by the combination of Big Data and internet scale may be like the flying cars we thought we'd see by 2014. Fortunately, this is is not the c... (more)

Four Myths of In-Memory Computing

Let's start at... the beginning. What is the in-memory computing? Kirill Sheynkman from RTP Ventures gave the following crisp definition which I like very much: "In-Memory Computing is based on a memory-first principle utilizing high-performance, integrated, distributed main memory systems to compute and transact on large-scale data sets in real-time - orders of magnitude faster than traditional disk-based systems." The most important part of this definition is "memory-first principle". Let me explain... Memory-First Principle Memory-first principle (or architecture) refers to a... (more)

Hadoop – 100x Faster

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)