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Update on Aug 2, 2019: Added to the end explaining our kernel patch and additional details we found. On June 17, three vulnerabilities in Linux’s networking stack were published. The most severe one could allow remote attackers to impact the system’s availability. We believe in offering the most secure image available to our customers, so...

Databricks is pleased to announce the release of Databricks Runtime 5.5 with Conda (Beta).  We introduced Databricks Runtime 5.4 with Conda (Beta), with the goal of making Python library and environment management very easy.  This release includes several important improvements and bug fixes as noted in the latest release notes [Azure|AWS].  We recommend all users...

Databricks is pleased to announce the release of Databricks Runtime 5.4.  This release includes Apache Spark 2.4.3 along with several important improvements and bug fixes .   We recommend all users upgrade to take advantage of this new runtime release.  This blog post gives a brief overview of some of the new high value features that...

On June 13th, we hosted a live webinar — Simplifying Streaming Stock Analysis using Delta Lake and Apache Spark — with Junta Nakai, Industry Leader - Financial Services at Databricks, John O’Dwyer, Solution Architect at Databricks, and Denny Lee, Technical Product Marketing Manager at Databricks. This is the first webinar in a series of financial...

In this blog post we introduce Databricks Connect, a new library that allows you to leverage native Apache Spark APIs from any Notebook, IDE, or custom application. Overview Over the last several years, many custom application connectors have been written for Apache Spark. This includes tools like spark-submit, REST job servers, notebook gateways, and so...

We are excited to announce the release of Databricks Runtime 5.4 ML (Azure | AWS). This release includes two Public Preview features to improve data science productivity, optimized storage in AWS for developing distributed applications, and a number of Python library upgrades. To get started, you simply select the Databricks Runtime 5.4 ML from the...

We are excited to introduce a new runtime: Databricks Runtime 5.4 with Conda (Beta). This runtime uses Conda to manage Python libraries and environments. Many of our Python users prefer to manage their Python environments and libraries with Conda, which quickly is emerging as a standard. Conda takes a holistic approach to package management by...

Spark + AI Summit 2019, the world’s largest data and machine learning conference for the Apache Spark™ Community, brought nearly 5000 registered data scientists, engineers, and business leaders to San Francisco's Moscone Center to find out what’s coming next. Watch the keynote recordings today and learn more about the latest product announcements for Apache Spark,...

MLflow is an open source platform to help manage the complete machine learning lifecycle. With MLflow, data scientists can track and share experiments locally or in the cloud, package and share models across frameworks, and deploy models virtually anywhere. Today at the Spark + AI Summit, we announced the General Availability of Managed MLflow on...

In less than a year, MLflow has reached almost 500K monthly downloads, and gathered over 80 code contributors and 40 contributing organizations, confirming the need for an open source approach to help standardize the machine learning lifecycle across tools, teams, and processes. We are thrilled to host some of our key contributors and customers next...