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Today, we announced the general availability of Databricks on Google Cloud, a jointly developed service that combines an open Lakehouse platform with an open cloud. Since the announcement of Databricks on Google Cloud, we have seen tremendous momentum for this partnership as customers pursue a multi-cloud approach to their analytics and DS/ML workloads. Customers are...

We’re excited to announce that PrivateLink connectivity for Databricks workspaces on AWS (Amazon Web Services) is now in public preview, with full support for production deployments. This release applies to all AWS regions supporting E2 architecture, as part of the Enterprise pricing tier. We have received great feedback from our global customers, including large financial...

This became the most-requested feature in Databricks’ history, and now it’s here: a dark theme for the Databricks notebook!  We’re excited for you to try it out. To turn it on, open a notebook and navigate to the View menu > Notebook Theme > Dark Theme. Enjoy!

With companies forced to adapt to a remote, distributed workforce this past year, cloud adoption has accelerated at an unprecedented pace by +14%  resulting in  2% or $13B above pre-pandemic forecasts for 2020 - with possibly more than $600B in on-prem to cloud migrations within the next few years.  This shift to the cloud places...

Databricks Jobs make it simple to run notebooks, Jars and Python eggs on a schedule. Our customers use Jobs to extract and transform data (ETL), train models and even email reports to their teams. Today, we are happy to announce a streamlined UI for jobs and new features designed to make your life easier. The...

Last month, we announced Databricks on Google Cloud, a jointly-developed service that allows data teams (data engineering, data science, analytics, and ML professionals) to store data in a simple, open lakehouse platform for all data, AI and analytics workloads. Today, we are launching the public preview of Databricks on Google Cloud. When speaking to our...

Most data science solutions make data teams choose between flexibility for exploration and rigidity for production. As a result, data scientists often need to hand off their work to engineering teams that use a different technology stack and essentially rewrite their work in a new environment. This is not only costly but also delays the...

More and more, we have seen the term “lakehouse” referenced in today’s data community. Beyond our own work at Databricks, companies and news organizations alike have increasingly turned to this idea of a data lakehouse as the future for unified analytics, data science, and machine learning. But what is a lakehouse? Join us for our...

We are excited to announce General Availability (GA) of the Microsoft Power BI connector for Databricks for Power BI Service and Power BI Desktop 2.85.681.0. Following the public preview, we have already seen strong customer adoption, so we are pleased to extend these capabilities to our entire customer base. The native Power BI connector for...

For years Hadoop was the default technology for big data analytics. But over time, it has fallen behind as new technologies have been introduced to provide better analytics solutions. Many organizations are looking at their Hadoop costs and trying to justify migrating to a modern cloud-based analytics platform. Databricks just released the whitepaper, “The Hidden...