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Items Tagged: GridGain

Profiles/Reports

Memory is the Hidden Secret to Success with Big Data: GridGain's In-Memory Hadoop Accelerator

Two big trends are driving IT today. One, of course, is big data. The growth in big data IT is tremendous, both in terms of data and in number of analytical apps developing in new architectures like Hadoop. The second is the well-documented long-term trend for critical resources like CPU and memory to get cheaper and denser over time. It seems a happy circumstance that these two trends accommodate each other to some extend; as data sets grow, resources are also growing. It's not surprising to see traditional scale-up databases with new in-memory options coming to the broader market for moderately-sized structured databases. What is not so obvious is that today an in-memory scale-out grid can cost-effectively accelerate both larger scale databases as well as those new big data analytical applications.

A robust in-memory distributed grid combines the speed of memory with massive horizontal scale-out and enterprise features previously reserved for disk-orienting systems. By transitioning data processing onto what's really now an in-memory data management platform, performance can be competitively accelerated across the board for all applications and all data types. For example, GridGain's In-Memory Computing Platform can functionally replace both slower disk-based SQL databases and accelerate unstructured big data processing to the point where formerly "batch" Hadoop-based apps can handle both streaming data and interactive analysis.

While IT shops may be generally familiar with traditional in-memory databases - and IT resource economics are shifting rapidly in favor of in-memory options - less known about how an in-memory approach is a game-changing enabler to big data efforts. In this report, we'll first briefly examine Hadoop and it's fundamental building blocks to see why high performance big data projects, those that are more interactive, real-time, streaming, and operationally focused have needed to continue to look for yet newer solutions. Then, much like the best in-memory database solutions, we'll see how GridGain's In-Memory Hadoop Accelerator can simply "plug-and-play" into Hadoop, immediately and transparently accelerating big data analysis by orders of magnitude. We'll finish by evaluating GridGain's enterprise robustness, performance and scalability, and consider how it enables a whole new set of competitive solutions unavailable over native databases and batch-style Hadoop.

Publish date: 07/08/14
news

GridGain In-Memory Computing Platform Enjoys Broad Adoption as a Result of its Recent Open Source

GridGain Systems (GridGain.com), provider of the leading open source In-Memory Computing (IMC) Platform, announced that since its open source announcement on March 3, customer interest in its products has significantly accelerated and product downloads have increased by more than 900 percent in the last four months.

  • Premiered: 07/31/14
  • Author: Taneja Group
  • Published: Broadway World
Topic(s): TBA GridGain TBA Mike Matchett TBA HPC TBA Big Data TBA analytics
Resources

Make the Elephant Fly: How to make Hadoop 10x faster in 10 minutes

Big data doesn't have to be slow. Now that you have identified and possibly even built functional Hadoop based big data applications that could deliver big value to your organizations, how can you easily make them work at operational speeds in production? Come listen in as Taneja Group Sr. Analyst Mike Matchett interviews Nikita Ivanov, CTO of GridGain, on why in-memory solutions for Hadoop can make all the difference for big data success. We'll explore what they do and how they work to speed up processing, how easy or hard they are to deploy and get working in the data center, and what the net impact can be. We'll also hope to talk about commercial vs. open source in the big data space, and what that means for the analytical community at large.

  • Premiered: 09/17/14
  • Location: OnDemand
  • Speaker(s): Mike Matchett, Sr. Analyst, Taneja Group; Nikita Ivanov, CTO of GridGain
  • Sponsor(s): GridGain, BrightTALK
Topic(s): TBA Topic(s): GridGain Topic(s): TBA Topic(s): Hadoop Topic(s): TBA Topic(s): BrightTALK Topic(s): TBA Topic(s): Mike Matchett Topic(s): TBA Topic(s): In-Memory Topic(s): TBA Topic(s): Big Data Topic(s): TBA Topic(s): Nikita Ivanov
news / Blog

GridGain Turns Over In-Memory Platform To Apache As Ignite Project

Recently I wrapped up a report on GridGain's In Memory Hadoop acceleration in which I explored how leveraging memory can vastly improve the production performance of many Hadoop MapReduce jobs, and even tackle streaming use cases without re-writing them or implementing newer streaming paradigms. GridGain drops into existing Hadoop environments without much fuss, so it's an easy add-on/upgrade. Now GridGain has just transferred the core in-memory platform over to Apache Software Foundation as the newly accepted Apache incubator Ignite project, completely contributed to the community at large.

  • Premiered: 11/06/14
  • Author: Mike Matchett
Topic(s): Big Data In Memory Computing GridGain Apache Ignite
news

Data World Needs a 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.

  • Premiered: 11/12/14
  • Author: Taneja Group
  • Published: Sys-Con Media
Topic(s): TBA GridGain TBA RAM TBA In-Memory TBA Big Data TBA Saas TBA mobile computing TBA Apache
news / Blog

IoT Goes Real-Time, Gets Predictive - Glassbeam Launches Spark-based Machine Learning

In-Memory processing was all the rage at Strata 2014 NY last month, and the hottest word was Spark! Spark is big data scale-out cluster solution that provides a way to speedily analyze large data sets in-memory using a "resilient distributed data" design for fault-tolerance. It can deploy into its own optimized cluster, or ride on top of Hadoop 2.0 using YARN... I haven't done justice to Spark itself and perhaps its biggest onrushing use case - taming the real-time data from from the Internet of Things (IoT)...

  • Premiered: 11/21/14
  • Author: Mike Matchett
Topic(s): Glassbeam Big Data Spark In Memory GridGain Machine Learning
news

Apache Ignite v1.0 Release Candidate By GridGain

Today, GridGain announces the first code drop of Apache Ignite, Apache Ignite v1.0 RC (Release Candidate).

  • Premiered: 02/18/15
  • Author: Taneja Group
  • Published: Sys-con
Topic(s): TBA GridGain TBA Apache TBA Apache Ignite TBA In-Memory TBA Storage TBA High Performance
news / Blog

In Memory Big Data Heats Up With Apache Ignite

Recently we posted about GridGain contributing their core in-memory solution to the Apache Ignite project. While this is still incubating, it's clear that this was a good move for GridGrain, and a win for the big data/BI community in general. Today Apache Ignite drops its v1.0 release candidate with some new features added in like built-in support for jCache and an autoloader to help migrate data and schema in from existing SQL databases (e.g. Oracle, MySQL, Postgres, DB2, Microsoft SQL, etc.).

  • Premiered: 02/19/15
  • Author: Mike Matchett
Topic(s): GridGain In Memory Big Data SQL