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Splice Machine Adds Support for Real-Time AI

New capabilities are included with version 3.1 of its SQL database.

Note: TDWI’s editors carefully choose vendor-issued press releases about new or upgraded products and services. We have edited and/or condensed this release to highlight key features but make no claims as to the accuracy of the vendor's statements.

Splice Machine, provider of a real-time AI platform built on a scale-out SQL database with built-in machine learning, released version 3.1 of its database, which introduces new features and functionality to support enterprises with real-time AI projects. With 3.1, Splice Machine has added Spark 3.0 support for its database engine, which adds performance improvements, resource elasticity support on Kubernetes, GPU support, and expansions to Spark's ML libraries.

Version 3.1 increases transparency of data used to create ML models. A new feature of 3.1 enables developers to query the database back in time with “AS OF” syntax to a specific date, providing a full audit and lineage for a regulator checking for bias or data drift.

Splice Machine has added new native Spark structured streaming ingestion, a feature that makes streaming data resources easier to ingest. This is especially valuable for industrial accounts connected to distributed control systems (DCs) and historians, where it is essential to ingest data in real-time as it becomes available.

Splice Machine 3.1 features enhanced database capabilities, including new foreign key processing, richer trigger support and improved handling, indexes on expressions, and improved import and export capabilities as well as DB2 compatibility. Splice Machine now enables migration of applications from IBM DB2 to a modern scale-out architecture with machine learning.

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