In this session, we discuss the lessons learned with Lara Minor, Senior Enterprise Data Manager at Columbia Sportswear, on how her team achieved a 70% reduction in pipeline creation time. This had reduced ETL workload times from four hours with previous data warehouses to minutes enabling near real-time analytics. Her team migrated from multiple legacy data warehouses, run by individual lines of business, to a single scalable, reliable, performant data lake.

See more at databricks.com/data-brew

Delta Lake is an open source storage layer that brings reliability to data lakes. Delta Lake offers ACID transactions, scalable metadata handling, and unifies streaming and batch data processing. It runs on top of your existing data lake and is fully compatible with Apache Spark APIs. For our “Demystifying Delta Lake” session, we will interview Michael Armbrust - committer and PMC member of Apache Spark™ and the original creator of Spark SQL. He currently leads the team at Databricks that designed and built Structured Streaming and Delta Lake.

See more at databricks.com/data-brew

Legacy approaches have failed to deliver on the promise of a single data architecture that can support every downstream use case from BI to AI. Lakehouse aspires to address this by combining the best of data warehouses and data lakes. Ali Ghodsi, Co-Founder and CEO of Databricks, and David Meyer, SVP of Product at Databricks, explain how.

See more at databricks.com/data-brew

In our inaugural episode, we’d like to welcome data warehouse luminaries Barry Devlin, Susan O’Connell, and Donald Farmer to discuss the evolution of data warehouses, data lakes, and lakehouses.

See more at databricks.com/data-brew