The lakehouse is a better data warehouse: 2026 benchmarks and proof

Summary

The data warehouse served structured SQL workloads for two decades, but the lakehouse now supports BI, data engineering, ML, streaming, and AI agents from a single governed platform. Databricks reports a 77% improvement in its production SQL workload mix since 2022, with customer migrations showing 90% faster queries (Lumen) and 80-90% faster analytics (Trek Bicycle). Lakehouse//RT delivers sub-second analytical queries at high concurrency directly on governed lakehouse tables, with Databricks reporting sub-100-ms latency at 12,000 queries per second, collapsing the traditional architecture of separate serving databases. The lakehouse eliminates data copying between systems, handles unstructured data, and runs on open formats like Delta Lake and Apache Iceberg, governed by Unity Catalog. Customer cost reductions range from 25% to 75%, with AXA Japan reducing warehouse costs by 60% and ETL costs by 70%, driven by eliminating redundant systems and data copies.

Takeaways

1. 1

   Databricks Lakehouse SQL performance improved 77% since 2022, with customer migrations showing 90% faster queries (Lumen) and 80-90% faster analytics (Trek Bicycle).

2. 2

   Lakehouse//RT delivers sub-second latency (as low as 10 ms) at up to 12,000 queries per second on governed lakehouse tables, eliminating the need for a separate OLAP serving layer.

3. 3

   Customer cost reductions from warehouse-to-lakehouse migration range from 25% to 75%, driven by eliminating redundant systems and data copies, not just cheaper queries.

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