Unified Analytics is a new category of solutions that unify data processing with AI technologies, enabling organizations to accelerate their AI initiatives.
Snowflake integration now available!
Get started quickly with out-of-the-box integration of TensorFlow
Fact: 96% cite data
challenges as #1 blocker
to AI success.
A new open source framework for the complete ML lifecycle
Make R programming simpler and more scalable with RStudio and Databricks.
A Gentle Introduction to Apache Spark™
Unifying Big Data and AI
Databricks Unified Analytics Platform, from the original creators of Apache Spark™, unifies data science and engineering across the Machine Learning lifecycle from data preparation, to experimentation and deployment of ML applications.
Speed up the preparation of high-quality data, essential for best-in-class ML applications, at scale.
Collaboratively explore large datasets, build models iteratively and deploy across multiple platforms.
“Databricks’ unified platform has helped foster collaboration across our data science and engineering teams which has impacted innovation and productivity.”
- John Landry, Distinguished Technologist at HP, Inc.
“We were able to take a tool that previously would have been fairly localised to a single region and turn that into a global product which actually is now becoming the foundation for the way our inventory analysts will now do their work.”
- Daniel Jeavons, General Manager Advanced Analytics CoE, Shell
“Databricks lets us focus on business problems and makes certain processes very simple. Now it’s a question of how do we bring these benefits to others in the organization who might not be aware of what they can do with this type of platform.”
- Dan Morris, Senior Director of Product Analytics , Viacom
“Databricks’ quality of support and how they’ve helped our team succeed is absolutely crucial for our business.”
- Matt Fryer, VP, Chief Data Science Officer, Hotels.com
“Working in Databricks is like getting a seat in first class. It’s just the way flying (or data science-ing) should be.”
- Mary Clair Thompson Data Scientist, Overstock.com