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)
Grid Dynamics, an eCommerce technology solutions company, and GridGain
Systems, makers of an open source in-memory platform for Big Data processing,
on Wednesday announced the expansion of their partnership which began in
Grid Dynamics provides personalization and big data solutions for large-scale
eCommerce companies, GridGain’s Java based open source middleware platform
allows organizations to perform real time processing and analytics on live
“GridGain has always been an important component within the portfolio of
technologies we use to build next-generation ... (more)
We have promised a while back to publish the code from live coding
GridGain presentation we did at QCon London earlier this year. Since
presentation was in Scala, the code we will be posting here is in Scala.
First a brief intro. We all know Hadoop’s counting words example which
takes a file with words and then produces another file with number
of occurrences next to each word. Hadoop does this example very well,
however the main caveat with Hadoop’s example is that it is not real time.
The counting words example we did at QCon actually counted words in real
time. The program wa... (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
GridGain will be hosting a webinar Thursday, July 26, 2012 at 3pm EST / 12pm
PST during which GridGain’s CTO, Dmitriy Setrakyan, will be live coding
GridGain examples in Java.
Dmitriy will cover the following examples:
How to distribute simple units of work to the grid. Collocation of
computation and data. A full Streaming MapReduce example that performs SQL
queries on streaming in-memory data.
This is a fantastic opportunity to see how easy it is to get started with
GridGain, so register now and join us for “Live Coding In-memory Bid Data