One cloud platform for massive scale data engineering and collaborative data science
Collaboration across the full data and machine learning lifecycle
Quickly access and explore data, find and share new insights, and build models collaboratively, with languages and tools of choice. Learn more about Notebooks.
One click access to preconfigured ML environments for augmented machine learning with state of the art and popular ML frameworks.
Learn more about ML Runtime.
Track and share experiments, reproduce runs, and manage models collaboratively from a central repository, from experimentation to production. Learn more about MLflow.
High quality data with great performance
Delta Lake brings data reliability and scalability to your existing data lake, with an open source transactional storage layer designed for the full data lifecycle. Learn more about Delta Lake.
Simple data processing on auto-scaling infrastructure. Powered by highly optimized Apache Spark™ for up to 50x performance gains. Learn more about Apache Spark.
Leverage your entire data lake, including streaming data, for the most complete BI reporting and visualizations.
A simple, scalable, and secure managed service
Effortless native security protects your data where it lives and creates compliant, private and isolated analytics workspaces across thousands of users and datasets. Learn more about Enterprise Security.
Audit and analyze all the activity in your account and set policies to administer users, control budget, and manage infrastructure for hassle-free enterprise-wide administration. Learn more about Simple Administration.
Run and scale your most mission-critical data workloads with confidence on a managed multi-cloud data platform, with ecosystem integrations for CI/CD and monitoring. Learn more about Production Ready Platform.
More complete and recent data to drive insights for every team
In this talk, Jim Forsythe and Jan Neumann describe Comcast’s data and machine learning infrastructure built on Databricks Unified Data Analytics Platform. Comcast uses Databricks to train and fuel the machine learning models at the heart of these products and gain deeper insights into how its users use these products.