CIO Survey: Top 3 Challenges Adopting AI and How to Overcome Them

Posted Leave a commentPosted in AI, Announcements, CIO, Company Blog, data, Events, ML, Product, Survey, Unified analytics

  We recently hosted the webinar — CIO Survey: Enterprise Challenges to AI and How to Overcome Them — featuring Jen Garofalo, Research Director at IDG, the parent company to CIO.com, and Pat McDonough, VP of Customer Success at Databricks. This webinar covered key findings from a recent CIO.com survey of 200 executives on the […]

Introducing Databricks Runtime 5.0 for Machine Learning

Posted Leave a commentPosted in Announcements, Company Blog, Databricks Runtime 5.0 ML, Deep Learning, Ecosystem, Engineering Blog, Machine Learning, Platform

Six months ago we introduced the Databricks Runtime for Machine Learning with the goal of making machine learning performant and easy on the Databricks Unified Analytics Platform. The Databricks Runtime for ML comes pre-packaged with many ML frameworks and enables distributed training and inference. Today we are excited to release the second iteration including Conda […]

Announcing Databricks Runtime 5.0 – The Databricks Blog

Posted Leave a commentPosted in Announcements, Apache Spark, Company Blog, Product

We’re excited to announce the general availability of Databricks Runtime 5.0. Included in this release is Spark 2.4. This release offers substantial performance increases within key areas of the platform. Benchmarking workloads have shown a 16% improvement in total execution time and Databricks Delta benefits from substantial improvements to metadata caching, improving query latency by […]

MongoDB Atlas: Connector for Apache Spark now Officially Certified for Azure Databricks

Posted Leave a commentPosted in Announcements, Azure, Company Blog, MongoDB, Partners

This is a guest blog from our partners at MongoDBBryan Reinero and Dana Groce We are happy to announce that the MongoDB Connector for Apache Spark is now officially certified for Azure Databricks. MongoDB Atlas users can integrate Spark and MongoDB in the cloud for advanced analytics and machine learning workloads by using the MongoDB […]

Databricks Engineering Interns & Impact in Summer 2018

Posted Leave a commentPosted in Announcements, Company Blog, Education, Intern

Thanks to our awesome interns! This summer, our Engineering interns at Databricks did amazing work.  Our interns, working on teams from Developer Tools to Machine Learning, built features and improvements which are already impacting our customers and the Apache Spark and AI communities. Spending a summer at Databricks Databricks Engineering internships are a mix of […]

MLflow v0.7.0 Features New R API by RStudio

Posted Leave a commentPosted in Announcements, Apache Spark, Company Blog, Deep Learning, Ecosystem, Education, Engineering Blog, GPyOpt, Hyperopt, Java, Keras, Machine Learning, MLflow, multistep workflow, Partners, python, R, RStudio

Today, we’re excited to announce MLflow v0.7.0, released with new features, including a new MLflow R client API contributed by RStudio. A testament to MLflow’s design goal of an open platform with adoption in the community, RStudio’s contribution extends the MLflow platform to a larger R community of data scientists who use RStudio and R […]

By Customer Demand: Databricks and Snowflake Integration

Posted Leave a commentPosted in Announcements, Partners, snowflake

Written By: Bill Chambers and Harsha Kapre Today, we are proud to announce a partnership between Snowflake and Databricks that will help our customers further unify Big Data and AI by providing an optimized, production-grade integration between Snowflake’s built for the cloud-built data warehouse and Databricks’ Unified Analytics Platform. Over the course of the last […]

Announcing Databricks Runtime 4.3 – The Databricks Blog

Posted Leave a commentPosted in Announcements, Company Blog, Engineering Blog, Platform, Product, Runtime

I’m pleased to announce the release of Databricks Runtime 4.3, powered by Apache Spark.  We’ve packed this release with an assortment of new features, performance improvements, and quality improvements to the platform.   We recommend moving to Databricks Runtime 4.3 in order to take advantage of these improvements. In our obsession to continually improve our platform’s […]