REDWOOD CITY, Calif. — Alation Inc. partnered with Databricks to accelerate data science-led innovations and foster a data culture. The new integration provides data teams with a platform to identify and govern cloud data lakes, discover and leverage the best data for data science and analytics, and collaborate on data to deliver high-quality predictive models and business insights.
Data teams establishing cloud analytics platforms need to increase performance and reduce cost while minimizing productivity loss and risk during the migration process. By identifying the most widely used assets, Alation enables data teams to prioritize data for migration to the cloud. Once in the cloud, Alation provides data teams with visibility into the assets residing in the data lake and allows for context and understanding of the data as well as collaboration among subject matter experts.
The partnership between Alation and Databricks enables organizations to do the following.
- Quickly identify and prioritize popular datasets for cloud data migration to manage an up-to-date cloud environment.
- Discover and understand data within Delta Lake on Databricks to enable the development of accurate data science and analytics.
- Collaborate with context and conversations in Alation to deliver trusted data for predictive models and business insights.
“We’re excited to partner with Databricks, as our customers are prioritizing data-driven decisions and need a way to efficiently discover, understand, and analyze data at scale,” said Kiran Narsu, vice president of business development, Alation. “In addition, as data teams move to the cloud, they can now identify and prioritize mission critical data for migration and diminish storage redundancies.”
“Databricks and Alation give customers visibility into high-quality data for analytics and data science projects,” said Michael Hoff, senior vice president of business development and partners, Databricks. “Data teams can now discover and understand the data in the Delta Lake on Databricks with Alation and use that data to improve the accuracy of predictive models.”