The Case for Moving to the Lakehouse

Available on demand

Running data warehouses for business analytics and data lakes for AI is costly, complex, inefficient — and unnecessary. In fact, as Large Language Model applications like ChatGPT disrupt everything, thousands of organizations are making generative AI their single biggest technological shift (and boardroom priority). The need to sync data between different systems to bring organizational-wide, high-quality data together has never been greater. 

Watch to learn about the lakehouse and why it's the right data architecture for all your data, analytics and AI use cases. Then we’ll cover integrating with modern tools you have today such as Fivetran and dbt, or new technologies you may adopt tomorrow, to access the freshest data from anywhere.

Learn from Databricks, Fivetran and dbt Labs experts how to:

1. Automate data movement and transform raw data into analytics-ready tables using your favorite tools like Fivetran and dbt
2. Unify and govern business-critical data at scale to build a curated data lake for data warehousing, SQL and BI
3. Reduce costs and get started in seconds with on-demand, elastic SQL serverless compute
4. Use automated and real-time lineage to monitor end-to-end data flow
5. Go from ingest to insight with Databricks SQL and Unity Catalog for best-in-class data warehousing, fine-grained governance, and sharing on the Lakehouse
6. Expand sharing and collaboration beyond just data with Delta Sharing and Databricks Marketplace