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 dat... (more)
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)
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
"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
Memory-first principle (or architecture) refers to a... (more)
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)
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.
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
Storage model doesn't dictate of preclude a particular transactionality or
consistency guarantees; there are columnar databases tha... (more)