Monitoring ML Models With Model Assertions
This is a collaborative post from Databricks and the Stanford University Computer Science Department. We thank Daniel Kang, Deepti Raghavan and Peter Bailis of Stanford University for their contributions. Machine learning (ML) models are increasingly used in a wide range of business applications. Organizations deploy hundreds of ML models to predict customer churn, optimal...
Introducing Databricks Unity Catalog: Fine-grained Governance for Data and AI on the Lakehouse
Data lake systems such as S3, ADLS, and GCS store the majority of data in today’s enterprises thanks to their scalability, low cost, and open interfaces. Over time, these systems have also become an attractive place to process data thanks to lakehouse technologies such as Delta Lake that enable ACID transactions and fast queries. However,...
Introducing Delta Sharing: An Open Protocol for Secure Data Sharing
Data sharing has become critical in the modern economy as enterprises look to securely exchange data with their customers, suppliers and partners. For example, a retailer may want to publish sales data to its suppliers in real time, or a supplier may want to share real-time inventory. But so far, data sharing has been severely...
An Update on Project Zen: Improving Apache Spark for Python Users
Apache Spark™ has reached its 10th anniversary with Apache Spark 3.0 which has many significant improvements and new features including but not limited to type hint support in pandas UDF, better error handling in UDFs, and Spark SQL adaptive query execution. It has grown to be one of the most successful open-source projects as the...
Spark + AI Summit Europe is Expanding and Getting a New Name: Data + AI Summit Europe
Back in 2013, we held the first Spark Summit — a gathering of the Apache Spark™ community with leading contributors and production users sharing their wisdom. Since the first event, Spark’s success has accelerated the evolution of data science, data engineering and analytics. As the data community has expanded, we’ve evolved the content and the...
Introducing the Next-Generation Data Science Workspace
At today’s Spark + AI Summit 2020, we unveiled the next generation of the Databricks Data Science Workspace: An open and unified experience for modern data teams. Existing solutions make data teams choose from three bad options. Giving data scientists the freedom to use any open-source tools on their laptops doesn’t provide a clear path...
MLflow Joins the Linux Foundation to Become the Open Standard for Machine Learning Platforms
Watch Spark + AI Summit Keynotes here At today's Spark + AI Summit 2020, we announced that MLflow is becoming a Linux Foundation project. Two years ago, we launched MLflow, an open source machine learning platform to let teams reliably build and productionize ML applications. Since then, we have been humbled and excited by the...
Introducing Apache Spark 3.0
We’re excited to announce that the Apache SparkTM 3.0.0 release is available on Databricks as part of our new Databricks Runtime 7.0. The 3.0.0 release includes over 3,400 patches and is the culmination of tremendous contributions from the open-source community, bringing major advances in Python and SQL capabilities and a focus on ease of use...
Evolving the Databricks brand
Some brands start out as, well, brands. A lot of work goes into the concept and painting the picture before the business is ever launched. Databricks is different. It always has been and always will be an engineering-led company. Databricks’ model for innovation is inspired by the open-source community. This is where our roots run...
What is a Lakehouse?
Over the past few years at Databricks, we've seen a new data management architecture that emerged independently across many customers and use cases: the lakehouse. In this post we describe this new architecture and its advantages over previous approaches. Data warehouses have a long history in decision support and business intelligence applications. Since its inception...
