In this webinar, attendees learned some of the best practices and the different options for maximizing availability and preventing data loss. This session explained in detail the various challenges including cluster and data center failures, and the best practices for implementing disaster recovery (DR) for distributed in-memory computing based on real-world deployments.

This webinar discusses deploying Apache Ignite into production in public and private clouds. Companies have faced many challenges when deploying in-memory computing platforms such as Apache Ignite in the cloud, but they have also discovered many best practices that have made success possible.

When you add a distributed in-memory computing cluster to support existing systems or new APIs, you introduce additional moving parts that can be hard to track and troubleshoot for performance issues or failures.

Learn how the veterans monitor various components of a distributed cluster for network, memory, or node-specific issues, and troubleshoot to resolve issues. By the end of this session you'll have a handy checklist and set of tools to consider using for your own deployments.

This session will cover:

This webinar discusses how an in-memory computing platform such as GridGain or Apache Ignite can modernize existing data lake architectures, enabling real-time analytics that spans operational, historical, and streaming data sets.

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. 

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. 

This session will dive into some of the best practices for both types of projects using Apache Ignite. 

Machine learning is a method of data analysis that automates the building of analytical models. By using algorithms that iteratively learn from data, computers can find hidden insights without the help of explicit programming. These insights bring tremendous benefits to 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.