---
title: "The lakehouse is a better data warehouse: 2026 benchmarks and proof"
description: "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."
---

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

[Databricks](https://yomu.fyi/company/databricks) · Joanna Zouhour · Oct 6, 2026

**Type:** Benchmark

## 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.

## Context

The post is a Databricks-authored argument that the lakehouse is a better data warehouse, using benchmarks, customer evidence, and architectural analysis. It addresses the historical strength of warehouses and shows how lakehouse SQL performance has caught up, then lists advantages beyond SQL such as serving AI agents, handling unstructured data, eliminating data copying, and supporting streaming.

## Approach / What changed

The Databricks Lakehouse architecture combines a data lake with warehouse capabilities, using Photon vectorized execution, Lakehouse//RT for sub-second serving, Unity Catalog for governance, and open table formats (Delta Lake, Apache Iceberg) to serve BI, ML, streaming, and AI workloads from a single governed platform.

## Takeaways

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

## FAQ

### Is the lakehouse actually faster than a data warehouse for SQL queries?

For most workloads, the lakehouse now matches or exceeds warehouse SQL performance. Databricks Lakehouse improved 77% since 2022, and independent TPC-DS testing shows sub-second p50 latency on 1-TB workloads at 74% lower cost. The warehouse may still edge ahead on highly concurrent short queries with warm caches, but the gap is narrow.

### How much can I save by migrating from a warehouse to a lakehouse?

Published customer results range from 25% to 75% cost reduction. AXA Japan reduced warehouse costs by 60% and ETL costs by 70%. Savings come from eliminating redundant systems and data copies, not just cheaper queries.

### Can the lakehouse handle real-time analytics?

Yes. Lakehouse//RT delivers sub-second analytical queries at high concurrency directly on governed lakehouse tables, with sub-100-ms latency at 12,000 queries per second. For streaming ingestion, Zerobus and Apache Spark Declarative Pipelines provide sub-second data freshness.

**Tags:** [Databricks](https://yomu.fyi/topic/databricks), [Data Warehousing](https://yomu.fyi/topic/data-warehousing), [Lakehouse](https://yomu.fyi/topic/lakehouse), [Real-Time Analytics](https://yomu.fyi/topic/real-time-analytics), [SQL](https://yomu.fyi/topic/sql)

- Source: [Databricks](https://www.databricks.com/blog/lakehouse-better-data-warehouse-2026-benchmarks-and-proof)
- Source URL: https://www.databricks.com/blog/lakehouse-better-data-warehouse-2026-benchmarks-and-proof
- Ingested by Yomu: 2026-10-06T21:00:07.222Z

[Read original post](https://www.databricks.com/blog/lakehouse-better-data-warehouse-2026-benchmarks-and-proof)