Lakehouse Architecture: From Vision to Reality

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SQL Analytics on Data Lakes offers up to 9x better price/performance for BI than traditional cloud data warehouses

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Enterprises today struggle with the complexity of maintaining both data lakes and data warehouses. They grapple with data silos that prevent a single source of truth, the expense of maintaining complicated data pipelines, and reduced decision-making speed.

The answer to this complexity is the lakehouse, a platform architecture that implements similar data structures and data management features to those in a data warehouse directly on the low-cost, flexible storage used for cloud data lakes. This new, simplified architecture allows traditional analytics and data science to co-exist in the same system.

To bring this to life, Databricks recently announced the new SQL Analytics service to provide customers with a first-class experience for performing BI and SQL workloads directly on the data lake, augmenting the rich data science and data engineering capabilities already available in the Databricks platform. With this launch, we are the first to realize the complete vision of lakehouse architecture to deliver 9x better price/performance than traditional cloud data warehouses.


  • Keynote and demo by CEO Ali Ghodsi and Brooke Wenig to introduce the SQL Analytics service and how it uniquely provides a first-class experience for BI and SQL on data lakes
  • Q&A with Unilever on how they are applying lakehouse architecture to reduce complexity and accelerate decision making
  • Partner talk with Tableau on the value of running BI workloads on the data lake
  • Technical Talks with Live Q&A answered throughout the event


Ali Ghodsi
CEO, Databricks

Brooke Wenig
Machine Learning Practice Lead, Databricks

Blair Hutchinson
Product Manager, Tableau

Todd Greenstein
Product Manager, Databricks

Daniel Ferrante
Director of Platform Engineering, Digital Turbine

Hosted by Denny Lee
Developer Advocate, Databricks