The GridGain Systems In-Memory Computing Blog

Monday, August 13, 2018
In this two-part series, we will look at how Apache® Ignite™ and Apache® Spark™ can be used together. Let's briefly recap what we covered in the first article. Ignite is a memory-centric distributed database, caching, and processing platform. It is designed for transactional, analytical, and streaming workloads, delivering in-memory performance at scale. Spark is a streaming and compute engine that typically ingests data from HDFS or other storage. Historically, it has been inclined towards OLAP and focussed on Map-Reduce payloads. The two technologies are, therefore, complementary. Using Ignite and Spark together Combining these two technologies provides Spark users with a number of significant benefits:
Technical Evangelist, GridGain Systems
Friday, August 10, 2018 | Akmal B. Chaudhri
Apache® Ignite™ is a very versatile product that supports a wide-range of integrated components. These components include a Machine Learning (ML) library that supports popular ML algorithms, such as Linear Regression, k-NN Classification and K-Means Clustering. The ML capabilities of Ignite provide a wide-range of benefits, as shown in Figure 1. For example, Ignite can work on the data in-place…
Wednesday, August 8, 2018 | Akmal B. Chaudhri
In this two-part series, we will look at how Apache® Ignite™ and Apache® Spark™ can be used together. Ignite is a memory-centric distributed database, caching, and processing platform. It is designed for transactional, analytical, and streaming workloads, delivering in-memory performance at scale. Spark is a streaming and compute engine that typically ingests data from HDFS or other storage.…
Tuesday, July 31, 2018 | Tom Diederich
Greetings! August is upon us and we’re organizing insightful yet fun events for you. Our experts will once again be on the road – traveling to San Diego, Silicon Valley, Atlanta and New York City.   But first, I’d like to offer a limited number of half-priced tickets to the In-Memory Computing Summit North America 2018. The conference is happening Oct. 2-3 near the San Francisco…
Thursday, July 19, 2018 | Akmal B. Chaudhri
In the previous article in this Machine Learning series, we looked at k-NN Classification with Apache® Ignite™. We’ll now look at another Machine Learning algorithm and conclude our series. In this article, we’ll look at K-Means Clustering using the Titanic dataset. Very conveniently, Kaggle provides the dataset in a CSV form. For our analysis, we are interested in two clusters: whether…
Wednesday, July 18, 2018 | Tom Diederich
In the first six months of 2018, GridGain Systems has clinched more new customer wins than in all of 2017; has launched major new products (including GridGain Professional Edition 2.4 and GridGain Cloud); has won several top industry accolades -- and has continued to drive growth of the popular In-Memory Computing Summit® in Europe. “GridGain is rapidly emerging as a leading software platform…
Monday, July 2, 2018 | Tom Diederich
The second-annual In-Memory Computing Summit Europe 2018 concluded last Tuesday. The two-day conference, June 25-26 at the Park Plaza Victoria London, presented attendees from across Europe with insights and practical advice for addressing digital transformation performance challenges. The many keynotes and breakout session from industry leaders and technology experts offered vital guidance on…
Friday, June 29, 2018 | Akmal B. Chaudhri
In the previous article in this Machine Learning series, we looked at Linear Regression with Apache® Ignite™. Now let’s take the opportunity to try another Machine Learning algorithm. This time we’ll look at k-Nearest Neighbor (k-NN) Classification. This algorithm is useful for determining class membership, where we classify an object based upon the most common class amongst its k nearest…
Thursday, June 21, 2018 | Tom Diederich
Summer begins today but things are not slowing down here on the community front! Last week, on June 13, GridGain’s Rob Meyer moderated a panel discussion around distributed systems and the future of in-memory computing at the Bay Area In-Memory Computing Meetup in Menlo Park, California. Denis Magda, director of product management and Apache Ignite PMC chair, sat on the panel. That's him to…
Thursday, June 14, 2018 | Tom Diederich
The beta release of GridGain Cloud was announced today. It's the only in-memory cache-as-a-service that allows users to rapidly deploy a distributed in-memory cache and access it using ANSI-99 SQL, key-value or REST APIs. Why is this huge? Because it gives users in-memory computing performance in the cloud, which can be massively scaled out and can be deployed in minutes for caching…
Wednesday, June 13, 2018 | Tom Diederich
Ravikanth Durgavajhala is an SD solutions architect focusing on Big Data & AI at Intel Corp. He’ll be speaking at the In-Memory Computing Summit Europe in London on June 25 at 11 a.m.  I connected with him recently about his session, “Expansion of System Memory using Intel Memory Drive Technology,” to share some insights in advance for those attending the conference – as well as for those…
Tuesday, June 12, 2018 | Tom Diederich
The full roster of keynotes for the second-annual In-Memory Computing Summit Europe, June 25-26 in London, was announced today. GridGain organizes the biannual summit (held in Silicon Valley in autumn and Europe in spring), and this month's successful event will be thanks to the conference committee, the speakers, sponsors -- and of course, none of it would be possible without the hard work of…
Thursday, June 7, 2018 | Akmal B. Chaudhri
In the previous article in this Machine Learning series, we looked at the Apache® Ignite™ Machine Learning Grid. Now let’s take the opportunity to drill-down further into some of the Machine Learning algorithms that are supported in Apache Ignite and try out some examples using popular datasets. If we search for suitable datasets to use, we can find many that are available. However, one dataset…
Wednesday, June 6, 2018 | Akmal B. Chaudhri
In a previous article, we discussed the Apache® Ignite™ Machine Learning Grid. At that time, a beta release was available. Subsequently, in version 2.4, Machine Learning became Generally Available. Since the 2.4 release, more improvements and developments have been added, including support for Partitioned-Based Datasets and Genetic Algorithms. Many of the Machine Learning examples that are…
Monday, June 4, 2018 | Rob Meyer
In case you hadn’t noticed, this year’s annual Spark conference is, for the first time, the Spark+AI Summit. The fact that Spark and AI should be together is predictable even without… using AI to figure it out. But there’s only one way to add continuous learning to Spark+AI, to make AI learn and adapt to new information in near real-time like a person. It is not the AllSpark, which is used to…
Friday, May 25, 2018 | Tom Diederich
As everyone makes their Memorial Day weekend plans here in the United States, I’m already looking ahead to next Wednesday’s Bay Area In-Memory Computing Meetup – a joint-event with the San Francisco Cloud Mafia.   I’m especially looking forward to this meetup because it will feature Nikita Ivanov, founder and CTO of GridGain Systems. Nikita is a fantastic speaker so it’s always great to…