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In many cases, the end of the year gives you time to step back and take stock of the last 12 months. This is when many of us take a hard look at what worked and what did not, complete performance reviews, and formulate plans for the coming year. For me, it is all of those things plus a time when I u...
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In-Memory Computing By @GridGain | @CloudExpo [#BigData]
The best way to clear the air around IMC is to start with a simple explanation of what, in fact, in-memory computing is

The Facts and Fiction of In-Memory Computing

In the last year, conversations about In-Memory Computing (IMC) have become more and more prevalent in enterprise IT circles, especially with organizations feeling the pressure to process massive quantities of data at the speed that is now being demanded by the Internet. The hype around IMC is justified: tasks that once took hours to execute are streamlined down to seconds by moving the computation and data from disk, directly to RAM. Through this simple adjustment, analytics are happening in real-time, and applications (as well as the development of applications) are working at-pace with this new standard of technology and speed.

Despite becoming both more cost-effective and accepted within enterprise computing, there are still a small handful of falsehoods that confuse even the most technical of individuals in enterprise IT.

Myth: In-memory computing is about databases, so this isn't really relevant to my business.

The best way to clear the air around IMC is to start with a simple explanation of what, in fact, in-memory computing is. While many assume that because we are talking about RAM, we are having a conversation about databases and storage, but this is not the case.

IMC, at its most basic level, is using a middleware software that allows one to store data in RAM - across a broad cluster of computers - and do any and all processing where it resides (in the memory). With traditional methods, data processing is often confined to spinning disks.

By comparison, in-memory computing speeds up this process by roughly 5,000 times. Now you can see that we're not talking about storage only - instead active, fluid data and computing.

Which brings me to another, more tangible point about computing efficiency. By incorporating in-memory, a handful of processes are streamlined in order to save time, resources, and money.

To start, in-memory requires much less hardware; the result - significantly decreased capital, operational and infrastructure overhead.

Moreover, IT departments can also significantly extend the life of existing hardware and software through the increased performance that is inherent with IMC - thus amplifying the ROI on the machines that have already been purchased.

Surprisingly, in-memory computing is not a new phenomenon. Since the inception of RAM, IMC has been viewed as reliable accelerant for high-performance computing, bringing us to the next crucial misconception about this technology.

Myth: In-memory computing is expensive, therefore not practical for my operation.

There is a reason that this is one of the most common misunderstandings about IMC, because there was a point in time where the cost of memory was once quite high. That being said, the cost of RAM has been dropping consistently, at a rate of about 30% - for the last 5 years.

Today, the price of a 1 Terabyte RAM cluster can go for anywhere between $20 and $40 thousand - including all of the CPUs, networking, etc. A few years from now that same setup will likely be available for half that price.

Regardless of the future price of RAM, which based upon current projections will likely continue to fall, the current economics have already placed this technology well within the reaches of the enterprise computing budgets that require this level of scale.

Myth: My needs are already being met by Flash.

There are three different reasons why this mentality is held by IT folks, each of which are highly misinformed. I'll start with the most common, which is the idea that your business doesn't need the Lambourgini-esque super-computing power of IMC.

The hard yet obvious reality is that if your business is in any way data-driven, you likely cannot survive without speed and agility in this department. As time goes on, the amount of data that businesses accumulate compounds with new streams and variances. This is a sink-or-swim reality.

Another myth commonly used to dispel IMC is that if businesses are able to just effectively mount RAM disk, they will get in-memory processing. Unfortunately, it's not that easy. As mentioned earlier, IMC works through middleware to effectively unlock its power.

Finally, there's the notion that one can just replace their HDDs with SSDs in order to get this super-charged performance. For SSDs - in certain situations - the performance gain that you can pull from flash storage in lieu of spinning disk is enough.

However, speed matters - and is rapidly becoming more of a requirement every day. At this point, it's like comparing apples to oranges with speed improvements of 10 to 100x over SSDs.

Myth: Memory is not durable enough to be truly sustainable.

This is another notion that for whatever reason has been both widely perpetuated - and is entirely false.

The fact is - almost all in-memory computing middleware (apart from very simplistic ones) offer one or multiple strategies for in-memory backups, durable storage backups, disk-based swap space overflow, etc.

More sophisticated vendors provide a comprehensive tiered storage approach where users can decide what portion of the overall data set is stored in RAM, local disk swap space or RDBMS/HDFS - where each tier can store progressively more data but with progressively longer latencies.

Yet another source of confusion is the difference between operational datasets and historical datasets. In-memory computing is not aimed at replacing enterprise data warehouse (EDW), backup or offline storage services - like Hadoop, for example. The goal of IMC is to improve the operational datasets that require mixed OLTP and OLAP processing and in most cases are less than 10TB in size. That is to say, in-memory computing is not "all or nothing" - and does not require that every aspect of data be housed in memory.

The in-memory computing revolution is by no means intended to obliterate disks from the enterprise. For now, the disk still serves a well-defined role for offline/backup use cases - tasks that are not the focus of IMC.

Myth: In-memory is inaccessible to my business because so few developers actually know how to use it.

Yes indeed, In-memory computing is a highly complex technology, that for now, only a few vendors have even been able to successfully develop offerings for. However, like much of high-technology, in-memory computing has entered the world of open source - bringing its capabilities and power to the fingertips of developers around the world.

Currently, with GridGain, developers have the ability to get their hands on IMC with a simple download at http://gridgain.org/.

In-memory computing is already being tapped across a broad range of functions and industries including (but not limited to) financial trading systems, online game, bioinformatics, hyper-local advertising, cognitive computing, and geospatial analysis.

By raising awareness, and bringing the capabilities of IMC to more developers and organizations - industries around the globe are poised to experience entirely new standards of speed, computing, and performance.

About Nikita Ivanov
Nikita Ivanov is founder and CEO of GridGain Systems, started in 2007 and funded by RTP Ventures and Almaz Capital. Nikita has led GridGain to develop advanced and distributed in-memory data processing technologies – the top Java in-memory computing platform starting every 10 seconds around the world today.

Nikita has over 20 years of experience in software application development, building HPC and middleware platforms, 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 Baltic State Technical University, Saint Petersburg, Russia.

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