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)
by Abe Kleinfeld and Nikita Ivanov
Gordon E. Moore's famously predicted tech explosion was prophetic, but it may
have hit a snag. While the number of transistors on integrated circuits has
doubled approximately every two years since his 1965 paper, the ability to
process and transact on data hasn't. We're now ingesting data faster than we
can make sense of it, leaving computing at an impasse. Without a new
approach, the innovation promised by the combination of Big Data and internet
scale may be like the flying cars we thought we'd see by 2014. Fortunately,
this is is not the c... (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)