Re-Imagining Ultimate Performance

It’s been somewhat quiet here and on GridGain side for a few months – and we’ve had a good reason for it.

We’ve just announced closing a $10M series B investment and bringing new awesome investor with it. In the last 6 months not only we’ve closed new round of investment, we’ve rebuilt and tripled our sales and business development team, retooled our marketing, released new products, and have 3 other products in the development pipeline to be announced this year. We’ve been busy…

But I think the most important thing we’ve accomplished so far is the crystallization and validation of our vision and strategy around end-to-end stack for In-Memory Computing.

In-Memory Computing

Kirill Sheynkman, one of our board members and investor, probably put it the best: “In-Memory Computing is characterized by using high-performance, integrated, distributed memory systems to manage and transact on large-scale data sets in real-time, orders of magnitude faster than possible with traditional disk-based technologies”.

And yet – In-Memory Computing is not a feature or just a product – it is a new way to compute and store data, a type of revolution we haven’t witnessed since the early seventies when IBM released “winchester” disk IBM 3340 and the era of HDDs has officially began. Today we are in same transitional period moving away from HDDs/SSDs or other block devices to a new era of DRAM-based storage and it creates a tidal wave of innovation in software.

Just as development of cheap HDDs pushed forward database industry in early seventies and the SQL was born – today the relentless data growth coupled with real time requirements for data processing necessitate move to in-memory processing, massive parallelization and unstructured data.

Unlike companies around us – here at GridGain we strongly believe that In-Memory Computing is a paradigm shift. It’s not just a single product, enhancement or feature add-on – it’s a new way to think about the ways we deal with exponentially growing data sets and the different types of payloads this data explosion brings in.

Here at GridGain we want to lead this revolution and we have the vision and technology to do just that…

End-to-End In-Memory Computing Stack

Most of today’s business applications dealing with large data sets (outside of legacy batch processing) are built around three fundamental types of payloads:

  • Database or system of records,
  • High performance, parallelized computations, and
  • Real time, high frequency streaming & CEP data processing.

These three types of payloads (or combination of them) is what at the core of practically every big data end user system built today. Providing in-memory products directly addressing these three types of payloads is what makes GridGain an end-to-end In-Memory Computing stack:


What also important is that everything that GridGain has was built in-house – without any exception. We didn’t acquire some fledging startup, got “merged” into something else, or acqua-hired some dying open source project to quickly fill the gap in the product line – every product we have is built by the same team, from the same mold and came about in course of natural evolution of our product line in the last 7 years.

That’s why you have absolutely zero learning curve when moving from product to product. Our customers often note just how cohesive and unified our products “feel” to them: familiar APIs, principles and concepts, same configuration, same management, same installation, same documentation… and the same engineers helping them with support.

Platforms don’t get built by haphazardly stitching together random pieces of software – they grow organically overtime by dedicated teams.

Integrated Products

Few years ago we’ve noticed one class of customers that would loved to get benefits of in-memory computing but just didn’t have the appetite for the development and simply shy away from using any in-memory computing products all together.

Instead of loosing these customers (like everyone else) we’ve decided to pick some of the most frequent use cases we come across and provide highly integrated, plug-n-play products for them, a.k.a accelerators, so that they can enjoy benefits of the in-memory computing without any need for any development or any changes in their systems what-so-ever.

That’s how In-Memory Hadoop® and In-Memory MongoDB® Accelerators came about. And that’s how cloud storage accelerators are coming about in a few months.

A unique characteristic of GridGain’s integrated products is the “no assembly required” nature in which they integrate. They deliver all the scalability and performance advantages of GridGain’s In-Memory Computing stack with zero code changes and minimal configuration changes to the host products.

Management & Monitoring

No end-to-end stack can be truly called this way without single and unified management and monitoring system. GridGain provides the #1 devops support among any in-memory computing products with its Visor Administration Console. GridGain’s Visor is GUI-based and CLI-based system that provides deep runtime management, monitoring, and operational support for running any GridGain products in production context.


Time Is Now

Einstein got it right when he said imagination is more important than knowledge. At GridGain, we’ve re-imagined ultimate performance as In-Memory Computing so that you can re-imagine your company for today’s increasingly competitive business environment.

GridGain understands that In-Memory Computing is more than the latest tech trend. It’s the next major shift for an increasingly hyper business world in which organizations face problems that traditional technology can’t even fathom, much less solve. In-Memory Computing is a step all organizations must take to remain competitive, and we’re ready to take that step with you.

You’ll never need to analyze less data. The speed of business will never be slower. Your business challenges will never be simpler. Now is the time for In-Memory Computing – only GridGain gives you a complete solution without any compromises.

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