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Apache Ignite: Troubleshooting best practices (Feb. 20 webinar)
Join one of GridGain's seasoned Customer Success gurus this Wednesday at 11 a.m. Pacific for a free one-hour webinar, Troubleshooting Apache® Ignite™. This live event is for you regardless of whether you are just getting started with Apache® Ignite™ or are a seasoned user. Register here.
Stanislav Lukyanov will be sharing some of the best practices that the GridGain® Customer Solutions team has used to troubleshoot hundreds of deployments. He'll share how to setup deployments to make them easier to monitor, manage and running properly.
In this session, you will see best practice examples on how to:
Previous Entries
Learn how Kubernetes can orchestrate a distributed database or in-memory computing solutions using Apache® Ignite™ as an example.
Denis Madga, GridGain's director of product management, took one of his most popular meetup talks and turned it into a webinar on July. And he recorded it! His talk is available for playback (along with his slides) here.
In-memory computing technologies such as in-…
Akmal Chaudhri, GridGain's technical evangelist, delivered an insightful webinar on July 3 that was the first of a two-part series called "In-Memory Computing Essentials for Architects and Developers."
This webinar, which was recorded and available for playback here (along with his slides), was so popular that he turned it into a hands-on workshop for several meetups around the world. So just…
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…
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…
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…
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…
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…
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…
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…
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…