MemSQL is simplifying machine learning in their v6 update

MemSQL showcased at the Strata Data Conference the ability to run machine learning (ML) algorithms in a distributed SQL environment. In the newest release of MemSQL 6, the company added new extensibility features to enable ML, massive performance improvements for analytical queries, and a broader set of online operations.

Previously, the path to implement ML meant working between different environments in the development process. Now, developers can use ML functions with live data and SQL, and a real-time data warehouse to easily build operational applications without requiring multiple disparate systems.

“To reach the point where machines learn more like a human brain, we must enable the learning to happen in real time,” said Nikita Shamgunov, CEO and co-founder, MemSQL. “Today, machine learning is still complex. MemSQL 6 closes the machine learning gap between data science and operational applications.”

To get the most out of query vectorization, MemSQL leverages modern chip architectures including Single Instruction, Multiple Data to improve their query processing rate. Additionally, the database provides efficient query isolation for improved concurrency on large data volumes and thousands of users, and boosted performance on encoded data for faster processing of financial, web, or sensor application workloads.

MemSQL also provides more robust cluster management that improves and simplifies resilience. 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.

Version 6 is available for testing and will be generally available for use in October 2017.