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
Skip to conclusion to get the bottom line.
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
Why the Fast Data World Needs a Proven and Mature In-Memory Data Fabric
Much of what human beings experience as commonplace today - social
networking, online gaming, mobile and wearable computing -- was impossible a
decade ago. One thing is certain: we're going to see even more impressive
advances in the next few years. However, this will be the result of a
fundamental change in computing, as current methods have reached their limit
in terms of speed and volume. Traditional disk-based storage infrastructure
is far too slow to meet today's data demands for speed at volume, which ar... (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)
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)
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)