We recently announced our AI-generated documentation feature , which uses large language models (LLMs) to automatically generate documentation for tables and columns in...
We are excited to announce that we have completed our acquisition of Arcion , a leading provider for real-time data replication technologies. Arcion’s...
Predictive Optimization intelligently optimizes your Lakehouse table data layouts for peak performance and cost-efficiency - without you needing to lift a finger.
We are excited to announce the public preview of the next generation of Databricks SQL dashboards, dubbed Lakeview dashboards . Available today, this...
We are thrilled to announce the opening of Databricks’ latest development center in Belgrade, Serbia. This addition joins our existing R&D centers in...
Today, we’re excited to share that we’ve completed our acquisition of MosaicML, a leading platform for creating and customizing generative AI models for...
Introduction We are thrilled to unveil the English SDK for Apache Spark, a transformative tool designed to enrich your Spark experience. Apache Spark™...
We are incredibly excited to announce that the team behind Rubicon is joining Databricks. Founded by large scale infrastructure builders, Akhil Gupta and...
Databricks SQL Serverless is now generally available. Read our blog to learn more. We are excited to announce the availability of serverless compute...
This week, many of the most influential engineers and researchers in the data management community are convening in-person in Philadelphia for the ACM...
Today we’re pleased to announce the availability of Databricks SQL in public preview on Google Cloud . With this announcement, customers can further...
Today, we are thrilled to announce that Databricks SQL is Generally Available (GA)! This follows our earlier announcements about Databricks SQL’s world record-setting...
We are excited to announce a new program called Databricks Engineering Fellowship to recognize new graduates with exceptional academic achievements or extracurricular impact...
Today, Databricks is known for our backend engineering, building and operating cloud systems that span millions of virtual machines processing exabytes of data...
Today we are excited to announce Brickchain , the next generation technology for zettabyte-scale analytics, by harnessing all the compute power on the...
Last week, the details of two industry-wide security vulnerabilities, known as Meltdown and Spectre , were released. These exploits enable cross-VM and cross-process...
We are excited to announce the general availability of Databricks Cache, a Databricks Runtime feature as part of the Unified Analytics Platform that...
At Databricks, our engineers guide thousands of organizations to define their big data and cloud strategies. When migrating big data workloads to the...
A major value Databricks provides is the automatic provisioning, configuration, and tuning of clusters of machines that process data. Running on these machines...
Developers have always loved Apache Spark for providing APIs that are simple yet powerful, a combination of traits that makes complex analysis possible...
Recommendation systems are among the most popular applications of machine learning. The idea is to predict whether a customer would like a certain item: a product, a movie, or a song. Scale is a key concern for recommendation systems, since computational complexity increases with the size of a company's customer base. In this blog post, we discuss how Apache Spark MLlib enables building recommendation models from billions of records in just a few lines of Pyt