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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)

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)

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)

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)

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)