As increasing numbers of industry leading companies look to new technologies
to speed their real-time services and Big Data applications, GridGain
Systems, leading developer of scalable, distributed in-memory compute and
data grid technologies for real-time data processing, is strongly positioned
to serve the demand.
This week, GridGain announced the release of an entirely new suite of
solution-focused products built around the company’s leading in-memory and
world’s fastest MapReduce technologies, expanded enterprise consulting and
other professional services, advanced DevOps management and monitoring
capabilities now available with all products, and out-of-box integration with
virtually any existing data source, Hadoop-based systems, and Java, C++,
.NET, Android and iOS applications.
Specifically related to GridGain’s new products, the company now offers
three w... (more)
We are happy to announce that GridGain 4.2 is released!
This release includes several new exciting feature as well as the host of
performance optimizations that we’ve included. This release is 100%
backward compatible with 4.x product line and we recommend anyone on 4.x
version to update as soon as possible.
Now – let’s talk about new features…
In GridGain 4.2 we’ve introduced support for delayed preloading. Dmitriy
Setrakyan wrote an excellent blog detailing this new functionality.
Essentially, whenever a new node joins the grid or an existing node leaves
I’m pleased to announce that today we released GridGain 4.0 – latest
edition of our platform for Real Time Big Data processing. I’m proud that
our team set this final deadline almost 5 months ago and we were able to hit
without a single delay.
I’m especially proud of this fact because of the enormous complexity of the
development process involved in making software like GridGain – dozens of
production clients, testing on serious massively distributed environments,
set of new features, and the usual array of setbacks that we had to go
through to get here.
Needless to say that w... (more)
Recently, at one of the customer meetings, I was asked whether GridGain comes
with its own database. Naturally my reaction was – why? GridGain easily
integrates pretty much with any persistent store you wish, including any
RDBMS, NoSql, or HDFS stores. However, then I thought, why not? We already
have cache swap space (disk overflow) storage based on Google LevelDB
key-value database implementation, so why not have the same for data store.
Here is how easy it was to add LevelDB based data store implementation for
GridGain cache – literally took me 20 minutes to do, including unit ... (more)
In-memory processing is becoming a business necessity in a similar way as
collecting and processing ever increasing data sets (a.k.a Big Data) has
become a business “must have” rather than just a simple technology in the
last five years. Both of these trends are intervened in an interesting ways.
Let me explain…
1. Storing Necessitates Processing
The initial foray into BigData for many companies was all about storing the
data and then some rudimentary processing that most of the time resulted in
some trivialized analytics run on log files, purchase history, and similar
type of d... (more)