Hybrid Transactional/Analytical Processing (HTAP) for OLTP and OLAP in One Platform
Hybrid transactional/analytical processing (HTAP) leverages the power of in-memory computing to bring OLTP and OLAP onto one platform. Before in-memory computing, transactional and analytical processes were split onto two separate platforms to reduce load on the transactional database. OLTP was run on an operational database. The transactional data was periodically loaded into a separate analytics database where often long-running OLAP queries could be run without slowing the transactional database.
Hybrid Transactional/Analytical Processing (HTAP) with In-Memory Computing
The GridGain® in-memory computing platform provides speed and scale to systems which integrate it into their architecture. By moving to an in-memory platform which increases application performance by up to 1,000x compared to performance based on disk-based databases, concerns about analytic processing slowing the transactional database are no longer an issue. Both transactions and analytics can be run on the same GridGain in-memory dataset with no negative impact on performance. In addition, the amount of data that can be held in-memory can be scaled out to handle petabytes of in-memory data simply by adding nodes to the GridGain cluster, allowing organizations to keep an arbitrarily large set of data in-memory to support analytical processing requirements. GridGain can be deployed on-premises, in private or public clouds, or in hybrid environments.
The GridGain in-memory computing platform is ANSI SQL-99 compliant, allowing users to easily run SQL analytical queries against the in-memory transactional dataset. Full SQL indexing and querying at in-memory speeds provides a powerful and fast solution for ad hoc or scheduled HTAP analytical jobs. GridGain SQL support includes support for DML and DDL.