New Advances in GridGain's Multi-Tier Database Engine

The latest release of the GridGain in-memory computing platform features enhanced support for the platform’s multi-tier database engine, that scales up and out across memory and disk. The changes enable customers to leverage the disk tier of the database engine to query much larger data sets, reduce cost of ownership, and secure sensitive or personal data at rest. Companies can use GridGain for a greater number of production use cases, ranging from complex real-time analytics to mission-critical transactional workloads

In this demo driven session, Valentin Kulichenko, Director of Product Management, will cover the following key enhancements:

  • Dictionary-based cache entry compression that allows data compression ratios of up to 60% on real-world scenarios.
  • Advanced seamless disk space defragmentation shrinks data files and reclaims disk space while still storing the current cached data durably on a disk.
  • SQL memory quotas on node or query level prevents out-of-memory errors when executing SQL queries requiring significant heap memory for execution.
  • Transparent data encryption improves security of data stored in the cluster by encrypting data at rest.
Valentin Kulichenko
Valentin Kulichenko
Director of Product Management at GridGain Systems

Valentin is a passionate, open-source Apache Ignite community member and a PMC member. He dedicates his time to public speaking, contributing code, and providing technical help through the dev and user mailing lists.