Events and Webinars

Learn some of the best practices and the different options for maximizing availability and preventing data loss.

Today, the majority of all Apache Ignite developers deploy Ignite in some sort of cloud, whether private on-premises clouds that use Kubernetes and Docker to simplify scalability, or in public clouds to reduce operational overhead. Nearly half of the people deploying Apache Ignite in the cloud are using or planning to use at least one of the major public clouds: Amazon Web Services (AWS), Google Cloud, Microsoft Azure, or Oracle Cloud. The challenge is how to preserve both speed and horizontal, elastic scalability together.

Data lakes, such as those powered by Hadoop, are an excellent choice for analytics and reporting at scale. Hadoop scales horizontally and cost-effectively and fulfills long-running operations spanning big data sets. However, the continual growth of real-time analytics requirements — where operations need to be completed in seconds rather than minutes, or milliseconds rather than seconds — has brought new challenges to Hadoop based solutions.

Learn how to 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.

Learn how companies have added speed and scale to MySQL deployments for different use cases. This webinar will cover the various options available and when each option makes sense. It will also cover how to evolve your architecture over time to add the speed, scale, agility and new technologies needed for digital transformation and other initiatives.

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. This webinar provides insights into the underlying architecture and best practices for implementing native persistence in production.