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.

Nikita Ivanov
Check out the GridGain in-memory computing meetup from August 20, 2019 featuring talks from Nikta Ivanov of GridGain Systems on distributed programming and Andy Revenes of Oracle on Oracle Database 12c.
Bay Area In-Memory Computing Meetup: Distributed Programming with In-Memory Computing, Oracle Databases 12c
Greg Stachnick
In this video from the Bay Area In-Memory Computing Meetup on Wednesday, July 17, 2019, GridGain's Director of Product Management Greg Stachnick, discusses some of the in-memory computing cloud deployment best practices for in-memory data grid (IMDG) and in-memory database (IMDB) in the cloud. 
Cloud Deployment Best Practices, Bay Area In-Memory Computing Meetup
Denis Magda
GridGain Meetups provide the in-memory computing community with a venue to discuss in-memory computing issues, solutions, and examples. Our summertime-themed edition Meetup on June 26, 2019, featured three talks on analytics from GridGain, Confluent, Oracle, and Alluxio.
NYC In-Memory Computing Meetup June 26, 2019
Valentin Kulichenko
This Bay Area In-Memory Computing Meetup featured talks on native persistence and data recovery, bringing data locality to your containerized big data workloads, and using in-memory computing to make money.
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Denis Mekhanikov
This IMCS Europe 2019 video discusses some best practices for monitoring distributed in-memory computing systems, including how to monitor applications, cluster logs, cluster metrics, operating systems, and networks. It provides guidance on tools like Elasticsearch, Grafana, and GridGain Web Console.
Best Practices for Monitoring Distributed In-Memory Computing Systems
Alexey Goncharuk
This IMCS Europe 2019 talk discusses the various components of Apache Ignite and GridGain, including memory storage, networking layer, compute grid, to help explain in-memory computing best practices for DevOps, high availability, proper testing, fault tolerance, and more.
The Insiders Checklist for Hardening an In-Memory Computing Cluster
Terry Erisman, Nikita Ivanov
This IMCS Europe 2019 keynote is a panel discussion of current and emerging trends in in-memory computing for enterprises looking to enable digital transformation.
In-Memory Computing Technology Trends: A Vendor Panel Discussion
Stephen Darlington
This IMCS Europe 2019 talk discusses migrating an in-memory computing platform to the cloud. It covers best practices, special considerations, tools, and differences between public and private clouds.
On Cloud Nine: How To Be Happy Migrating Your In-Memory Computing Platform to the Cloud
Nikita Ivanov
Cloud computing has been growing rapidly. The widespread availability of non-volatile RAM products is imminent. In-memory computing is facing a rapidly evolving technology landscape. At the same time, digital transformation is unleashing massive quantities of data, often streaming data, which requires real-time ingestion, processing, and analysis.
The Future of In-Memory Computing in a Rapidly Changing World
Yury Babak
The current implementation of ML algorithms in Spark has several disadvantages associated with the transition from standard Spark SQL types to ML-specific types, a low level of algorithms’ adaptation to distributed computing, a relatively slow speed of adding new algorithms to the cu
Stacking, Boosting, and Online Learning in distributed mode with Apache Ignite
Denis Magda
This talk demonstrates how to implement integrating Apache Kafka with Apache Ignite in practice, explains the architectural reasoning and the benefits of real-time integration, and shares common usage patterns. The presenters build a streaming data pipeline using nothing but their bare hands, Apache Ignite, Kafka Connect, and KSQL.
How-To for Real-Time Alerting, Analytics, and Reporting at Scale with Apache Kafka and Apache Ignite
Ivan Rakov
As an in-memory computing platform, GridGain and Apache Ignite support native persistence that stores data and indexes transparently on non-volatile memory, SSD or disk. When persistence is enabled, memory becomes a cache for the most frequently used data and indexes. Native persistence is ACID-compliant, durable and enables immediate availability on a restart of…
In-Memory Computing Meets Database Durability: Best Practices for Native Persistence and Data Recovery
Valentin Kulichenko
Over the last decade, the 10x growth of transaction volumes, 50x growth in data volumes, and drive for real-time response and analytics has pushed relational databases beyond their limits. Scaling an existing RDBMS vertically with hardware is expensive and limited.
How to Add Speed and Scale to SQL, Support New Data Needs, and Keep Your RDBMS
Denis Magda
In this IMCS Europe 2019 session, Denis Magda describes how Apache Ignite and GridGain as an in-memory computing platform can modernize existing data lake architectures, enabling real-time analytics that spans operational, historical, and streaming data sets.
Enabling Real-Time Analytics for Hadoop Data Lakes with GridGain
Akmal B. Chaudhri
Talk 1: In this talk, learn how to use an Application Tier Database Cache with SQL. Writing a naive SQL read or write cache is easy. Writing a SQL read/write cache with cache coherency, concurrency, ACID transactions and high availability is hard. Having your SQL read/write cache enable a latency of less than 1 ms at the 99th percentile without any user code is very…
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