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.


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. 
technical_presentation thumbnail
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
technical_presentation thumbnail
Learn how Kubernetes can orchestrate a distributed database or in-memory computing solutions using Apache Ignite as an example.
technical_presentation thumbnail
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.
technical_presentation thumbnail
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…
technical_presentation thumbnail
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:
technical_presentation thumbnail
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.
technical_presentation thumbnail
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™.
technical_presentation thumbnail
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.
technical_presentation thumbnail
How we accelerated the Machine Learning in Apache Ignite.
technical_presentation thumbnail
Learn why companies are choosing Apache Ignite and the enterprise-ready version of Apache Ignite from GridGain® to handle their in-memory computing needs, and moving away from traditional caches like Redis.
technical_presentation thumbnail
Learn how to get Apache Ignite native persistence up and running, and tips and tricks to get the best performance.
technical_presentation thumbnail
Apache Ignite is (an in-memory computing platform OR an in-memory distributed data store and compute grid) with full-fledged SQL, key-value and processing APIs. Many companies have added it as a cache in-between existing SQL databases and their applications to speed up response times and scale. In other projects they've used it as its own SQL database. 
technical_presentation thumbnail