Nikita Ivanov

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Nikita Ivanov

Position:

Founder & CTO, GridGain Systems

Bio:

Nikita Ivanov is founder and CTO of GridGain Systems, started in 2007 and funded by RTP Ventures and Almaz Capital. Nikita provides the vision and leadership at GridGain to develop the world’s top in-memory computing platform, now used by thousands of organizations around the globe to power business-critical systems and enable digital transformation initiatives.

Nikita has over 20 years of experience in software application development, building HPC and middleware platforms, and contributing to the efforts of other startups and notable companies including Adaptec, Visa and BEA Systems. Nikita was one of the pioneers in using Java technology for server side middleware development while working for one of Europe’s largest system integrators in 1996.

He is an active member of Java middleware community, contributor to the Java specification, and holds a Master’s degree in Electro Mechanics from the Baltic State Technical University, Saint Petersburg, Russia.

Bio:

Nikita Ivanov is founder and CTO of GridGain Systems, started in 2007 and funded by RTP Ventures and Almaz Capital. Nikita provides the vision and leadership at GridGain to develop the world’s top in-memory computing platform, now used by thousands of organizations around the globe to power business-critical systems and enable digital transformation initiatives.

Nikita has over 20 years of experience in software application development, building HPC and middleware platforms, and contributing to the efforts of other startups and notable companies including Adaptec, Visa and BEA Systems. Nikita was one of the pioneers in using Java technology for server side middleware development while working for one of Europe’s largest system integrators in 1996.

He is an active member of Java middleware community, contributor to the Java specification, and holds a Master’s degree in Electro Mechanics from the Baltic State Technical University, Saint Petersburg, Russia.

Debunking DRAM vs. Flash Controversy vis-a-vis In-Memory Processing

Nikita Ivanov

Debunking DRAM vs. Flash Controversy vis-a-vis In-Memory Processing

Wikibon produced an interesting material (looks like paid by Aerospike, NoSQL database recently emerged by resurrecting failed CitrusLeaf and acquihiring AlchemyDB, which product, of course, was recommended in the end) that compares NoSQL databases based on storing data in flash-based SSD vs. storing data in DRAM. There are number of factual problems with that paper and I want to point them out…

GridGain & Hadoop: Differences & Synergies

Nikita Ivanov

GridGain & Hadoop: Differences & Synergies

GridGain & Hadoop: Differences & Synergies

Overview GridGain is Java-based middleware for in-memory processing of big data in a distributed environment. It is based on high performance in-memory data platform that integrates fast In-Memory MapReduce implementation with In-Memory Data Grid technology delivering easy to use and easy to scale software. Using GridGain you can process terabytes of data, on 1000s of nodes in under a second.…

In-Memory Compute Grid... Explained.

Nikita Ivanov

In-Memory Compute Grid... Explained.

In-Memory Compute Grid... Explained.

Dmitriy Setrakyan provided an excellent explanation for in-memory data grids (IMDG) in his blog In-Memory Data Grids... Explained. I will try to provide a similar description for in-memory compute grid (IMCG). Learn more about GridGain in-memory compute grids here. IMCG - In-Memory Compute Grid One of the main ideas Dmitriy put forward is the importance of integration between in-memory…

Berkeley Researchers Highlight Emergence of In-Memory Processing

Nikita Ivanov

Berkeley Researchers Highlight Emergence of In-Memory Processing

Berkeley Researchers Highlight Emergence of In-Memory Processing

Excellent paper released by researchers at University of California, Berkeley . They have analyzed data from Hadoop installation at Facebook (one of the largest as such in the world) looking at various metrics for Hadoop jobs running at Facebook datacenter that has over 3,000 computers dedicated to Hadoop-based processing. They have come up with very interesting insights. I advise everyone…

Practical Introduction to Streaming MapReduce

Nikita Ivanov

Practical Introduction to Streaming MapReduce

Practical Introduction to Streaming MapReduce

In this article I’ll introduce the concept of Streaming MapReduce processing using GridGain and Scala. The choice of Scala is simply due to the fact that it provides for very concise notation and GridGain provides very effective DSL for Scala. Rest assured you can equally follow this post in Java or Groovy just as well. The concept of streaming processing (and Streaming MapReduce in particular…

In-Memory Processing As A Business Use Case

Nikita Ivanov

In-Memory Processing As A Business Use Case

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…

Micro cloud in your JVM: code example…

Nikita Ivanov

Micro cloud in your JVM: code example…

Few days ago I blogged about how GridGain easily supports starting many GridGain nodes in the single JVM - which is a huge productivity boost during the development. I've got a lot of requests to show the code - so here it is. This is an example that we are shipping with upcoming 4.3 release (entire source code): [source lang="java" title="Micro cloud example"] import org.gridgain.grid.*; import…

Micro cloud in your JVM with GridGain.

Nikita Ivanov

Micro cloud in your JVM with GridGain.

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's node 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…

Five tell telling “words” that your analytics/BI strategy is rotten...

Nikita Ivanov

Five tell telling “words” that your analytics/BI strategy is rotten...

Five tell telling “words” that your analytics/BI strategy is rotten...

Over the last 12 months I’ve accumulated plenty of “conversations” where we’ve discussed big data analytics and BI strategies with our customers and potential users. These 5 points below represent some of the key take-away points about current state of analytics/BI field, why it is by in large a sore state of affairs and what some of the obvious tell telling signs of the decay. Beware: some…

Adding LevelDB store for your In-Memory Cache?

Nikita Ivanov

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…

What does "Real Time" in "Real Time Big Data" mean?

Nikita Ivanov

What does "Real Time" in "Real Time Big Data" mean?

What does "Real Time" in "Real Time Big Data" mean?

Lately there has been lots of noise about "Real Time" Big Data. Lots of companies that associate themselves with this term are generally in analytical space and to them it really means "low-latency" for analytical processing of data which is usually stored in some warehouse, like Hadoop Distributed File System (HDFS). To achieve this they usually create in-memory indexes over HDFS data which…

GridGain 4.0.1 Released!

Nikita Ivanov

GridGain 4.0.1 Released!

GridGain 4.0.1 Released!

GridGain 4.0.1 has been released this Monday. This is a point release that includes several bug fixes as well as number of new features. With 4.0.1 we are introducing native support for .NET with our C# Client. C# Client provides native .NET/C# APIs for accessing both GridGain's In-Memory Data Grid and Compute Grid from outside of the GridGain topology context. Internally it's deferring to…

Counting Words In Real Time On GridGain

Nikita Ivanov

Counting Words In Real Time On GridGain

Counting Words In Real Time On GridGain

We have promised a while back to publish the code from live coding GridGain presentation we did at QCon London earlier this year. Since presentation was in Scala, the code we will be posting here is in Scala.First a brief intro. We all know Hadoop's counting words example which takes a file with words and then produces another file with number of occurrences next to each word.…

GridGain and Hadoop

Nikita Ivanov

GridGain and Hadoop

GridGain and Hadoop

Over the past few months I’ve been repeatedly asked on how GridGain relates to Hadoop. Having been answering this questions over and over again I’ve compacted it to just few words:We love Hadoop HDFS, but we are sorry for people who have to use Hadoop MapReduce.Let me explain.Hadoop HDFSWe love Hadoop HDFS. It is a new and improved version of enterprise tape drive. It is an excellent technology…

Grid Computing and AOP

Nikita Ivanov

Grid Computing and AOP

One of the unique features of our upcoming GridGain 1.0 product is AOP-based integration.Since the beginning, the integration part of Grid Computing products was a sore point. APIs are complex, non-intuitive and vendor generally assumed that people writing grid-enabled software would be willing to spend a significant time on grid-enabling process (changing their applications, learning complex…