MemSQL, provider of a real-time data warehouse platform, is releasing MemSQL 6, adding new extensibility features to enable machine learning and performance improvements for analytical queries.
With this update, developers can use machine learning functions with live data and SQL, and a real-time data warehouse to easily build operational applications without requiring multiple disparate systems.
MemSQL 6 includes the ability to run machine learning algorithms in a distributed SQL environment, enhancements to online operations, and increases to query performance to deliver up to 80 times improvement from previous versions.
For companies wanting to bring machine learning functions closer to live data, MemSQL 6 supports real-time scoring with existing or new models, DOT_PRODUCT for image recognition, and new extensibility capabilities that enable functions, such as K-means in SQL.
Specific to query performance, MemSQL leverages modern chip architectures including Single Instruction, Multiple Data for breakthrough performance achieving a query processing rate of one billion rows per CPU core per second.
Additionally, MemSQL provides efficient query isolation for improved concurrency on large data volumes and thousands of users, and higher performance on encoded data for faster processing of financial, web, or sensor application workloads.
For enhanced online operations, MemSQL provides more robust cluster management that improves and simplifies resilience, and new manageability options, including expanded online coverage for DDL, upgraded availability and optimized recovery time. Comprehensive security protects against internal and external threats with sophisticated role-based access control.
Moving forward the company will look to integrate more AI components to assist with machine learning capabilities, according to Gary Orenstein, chief marketing officer at MemSQL.
For more information about this update visit www.memsql.com.