Technical Presentations About GridGain and Apache Ignite

The technical presentations below will help you understand, configure, and deploy the GridGain® and Apache® Ignite in-memory computing solutions. The presentations cover topics including in-memory computing, in-memory databases, stream processing, data lake acceleration, machine learning, deep learning, and how to apply these technologies and others to power your organizations’ digital transformation. These technical presentations have been developed and presented by GridGain in-memory computing experts.


This presentation covers best practices for delivering new applications and APIs with in-memory computing, and how it helps open up existing systems, become more agile, and deliver unlimited speed and scale.
This presentation explains the best practices for adding speed and scale to existing applications that offer the least disruption and help meet the long term goals of transforming the business.
Learn how companies have been using Apache® Ignite™ to add in-memory speed and unlimited horizontal scale to SQL with no rip-and-replace of the underlying database. This session will explain how to:
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This presentation covers some of the best practices companies have used for making Apache Ignite and Apache Kafka scale. Making stream processing scale requires making all the components—including messaging, processing, storage—scale together. 
With real-time streaming analytics there is no room for staging or disk.  Learn the best practices used for real-time stream ingestion, processing and analytics using Apache® Ignite™, GridGain®, Kafka™, Spark™ and other technologies. 
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This presentation will provide a better understanding about best practices for designing multi-cluster in-memory computing applications. It covers....
This 60-minute hands-on session will: Explain how GridGain Cloud works Walk through step-by-step how to create an account, start a cluster, load and query data using ANSI-99 SQL Demonstrate how to build applications that accesses data using a REST API, ODBC/JDBC or a binary thin client
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Learn how Kubernetes can orchestrate a distributed database or in-memory computing solutions using Apache Ignite as an example.
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When deployed properly, it's hard to beat a horizontal distributed architecture's scalability, availability and reliability. The trick is deploying it properly to compensate for individual node, data or network failures. Learn some of the best practices for setting up your clusters properly to maximize availability, reliability and flexibility for future needs.
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Once you've put in-memory computing in place to add speed and scale to your existing applications, the next step is to innovate and improve the customer experience. Join us for part 2 of the in-memory computing best practices series. Learn how companies build new HTAP applications with in-memory computing that leverage analytics within transactions to improve…
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Join us for part 1 of the in-memory computing best practices series. Learn how companies are not only adding speed and scale without ripping out, rewriting or replacing their existing applications and databases, but also how they're setting themselves up for future projects to improve the customer experience. This webinar will explain with examples:
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Learn more about the role of In-Memory Computing in supporting the real-time transactional, analytical and engagement needs for digital business and improving the customer experience.
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Learn some of the current best practices in HTAP, and the differences between two of the more common technologies companies use: Apache® Cassandra™ and Apache® Ignite™.
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By the end of the webinar, you will understand the most common in-memory computing options and how to choose the right in-memory technology based on your project needs.
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How we accelerated the Machine Learning in Apache Ignite.
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