GridGain and Apache Ignite In-Memory Computing Videos

Learn about GridGain® and Apache® Ignite by watching this library of in-memory computing videos captured during speaking engagements at conferences, Meetups, and webinars.


Denis Magda
Machine learning is a method of data analysis that automates the building of analytical models.  These insights bring tremendous benefits into many different domains. For business users, in particular, these insights help organizations improve customer experience, become more competitive, and respond much faster to opportunities or threats. 
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Dmitriy Setrakyan
Presented by Dmitriy Setrakyan of GridGain at the Bay Area Hadoop Meetup in Sunnyvale, CA on August 17, 2016.
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Nikita Ivanov
This presentation provides a deep dive into .NET features of the top level Apache projects: Apache Ignite. Apache Ignite is the in-memory data fabric that combines in-memory cluster & computing, in-memory data grid and in-memory streaming under one umbrella of a fabric.
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Nikita Ivanov
This talk was presented at the "SQL NYC, the NoSQL and NewSQL Database Meetup." It provides a deep dive into Apache Ignite, a top-level Apache project.
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Dmitriy Setrakyan
Speakers: Dmitriy Setrakyan, Founder and Chief Product Officer, GridGain and Mikhail Khasin, Senior Managing Director & Head of Core banking Transformation Program, Sberbank
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Nikita Ivanov
Speaker: Nikita Ivanov, Founder and CTO GridGain Systems In this talk I'll present the SharedRDD - a high-performance in-memory caching layer for Spark jobs. We'll work through 1) design & architecture of this component, 2) configuration and 3) actual Java and Scala usage examples.
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Nikita Ivanov
GridGain CTO Nikita Ivanov gives a brief introduction to the GridGain In-Memory Data Fabric at the O'Reilly Strata+Hadoop World conference. He provides a brief history of the development of the product along with a high-level description of how the In-Memory Data Fabric accelerates and scales applications across distributed systems regardless of data sources or…
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Christos Erotocritou
In this live presentation at a Meetup in London Christos Erotocritou, Solutions Architect at GridGain provides an introduction to Apache Ignite™ to Java developers. During this session you’ll learn about the Apache Ignite project and how it is used to solve some of the most demanding scalability and performance challenges.
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Konstantin Boudnik
Dr. Konstantin Boudnik, co-founder and CEO of Memcore (memcore.io), will start with a brief overview of Apache Ignite and Apache Bigtop projects for those who are new to them. He will then conduct a live demo of complex analytic with new Memcore cloud platform, utilizing Apache Ignite in-memory accelerator.
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Dmitriy Setrakyan
In this live presentation at a Bay Area Meetup Dmitriy Setrakyan, Co-founder and EVP of Engineering at GridGain and Chairman of the Apache Ignite™ PMC talks about the advanced SQL capabilities of Apache Ignite, including support for distributed joins, custom SQL functions, and others.
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Nikita Ivanov
Nikita Ivanov, GridGain co-founder and CTO, provides insight on choosing the best in-memory solution for your apps in a recent Meetup in the Bay Area. He discusses different types of in-memory systems, including RDBMS in-memory options, in-memory databases, data grids and data fabrics, while taking several questions from the audience.
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Dmitriy Setrakyan
This is a 40-minute sessions by Dmitriy Setrakyan, GridGain Systems EVP Engineering and Project Committee Member for Apache Ignite™ delivered at the In-Memory Computing Summit in June 2015.
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Nikita Ivanov
This is a general session delivered by Nikita Ivanov, GridGain Systems CTO and Project Committee Member for Apache Ignite™ delivered at the In-Memory Computing Summit in June 2015.
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Abe Kleinfeld
The In-Memory Computing Summit 2015 is the first and only industry-wide event of its kind, tailored to in-memory computing related technologies and solutions. Check out the highlights from last year’s event.
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Dmitriy Setrakyan
Part 2 of Dmitriy Setrakyan’s introduction of the Apache Ignite™ In-Memory Data Fabric shortly after the initial release.
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