by Angela Guess

According to a recent press release, “Databricks, provider of the leading Unified Analytics Platform and founded by the team who created Apache Spark™, today announced the availability of Apache Spark 2.3.0 on Databricks’ Unified Analytics Platform. Databricks is the first vendor to support Apache Spark 2.3 within a compute engine, Databricks Runtime 4.0, which is now generally available. In addition to support for Spark 2.3, Databricks Runtime 4.0 introduces new features including Machine Learning Model Export to simplifying production deployments and performance optimizations. The Apache Spark community made multiple valuable contributions to the Spark 2.3 release which was introduced on February 28.”

Matei Zaharia, creator of Apache Spark and chief technologist and co-founder of Databricks, noted, “The community continues to expand on Apache Spark’s role as a unified analytics engine for big data and AI. This is a major milestone to introduce the continuous processing mode of Structured Streaming with millisecond low-latency, as well as other features across the project… By making these innovations available in the newest version of the Databricks Runtime, Databricks is immediately offering customers a cloud-optimized environment to run Spark 2.3 applications with a complete suite of surrounding tools.”

The release goes on, “The Databricks Runtime, built on top of Apache Spark, is the cloud-optimized core of the Databricks Unified Analytics Platform that focuses on making big data and artificial intelligence simple for enterprise organizations. The enhancements introduced in the Spark 2.3, which is supported within the latest Databricks Runtime 4.0, focus on usability, stability, and refinement. In addition to introducing stream-to-stream joins and extending new functionality to SparkR, Python, MLlib, and GraphX, the new release provides a millisecond-latency Continuous Processing mode for Structured Streaming.”

Read more at Globe Newswire.

Photo credit: Databricks