The Data Warehouse and the Data Lakehouse both answer analytical SQL queries. The similarities end there. The two architectures make fundamentally different decisions about where data lives, how it is stored, who can run engines against it, and how much it costs to operate at scale. Understanding those differences is the prerequisite for any serious enterprise data platform decision in 2025 and beyond.
Side-by-Side Comparison
| Dimension | Data Warehouse | Data Lakehouse |
|---|---|---|
| Storage Format | Proprietary columnar format (vendor-specific) | Open Parquet files in cloud object storage |
| Compute | Tightly coupled to the vendor's engine | Any compatible engine (Dremio, Spark, Trino, DuckDB) |
| Data Types | Primarily structured tabular data | Structured, semi-structured, and unstructured |
| Storage Cost | High (proprietary compressed storage) | Low (commodity S3/ADLS at cents per GB) |
| ML/AI Workloads | Requires data export to external systems | Native: ML tools read Iceberg files directly |
| Schema Flexibility | Rigid DDL changes require migration scripts | Schema evolution built into Iceberg spec |
| Vendor Lock-in | High: data format and compute are bundled | Low: open formats allow engine substitution |
| Time Travel | Vendor-specific, often expensive add-on | Built into Apache Iceberg's snapshot model |
| Governance | Centralized within vendor platform | Apache Polaris or other open catalog |
When a Data Warehouse Still Makes Sense
Cloud data warehouses like Snowflake and BigQuery remain excellent choices for teams that need minimal operational overhead and whose data footprint fits comfortably within proprietary storage tiers. If all analytics workloads are SQL-based BI, data volumes are in the low terabyte range, and there is no need for external engine access or ML training data pipelines, a managed cloud data warehouse can be the lower-friction option.
When the Lakehouse Is Required
The Lakehouse becomes the correct architecture when any of the following are true: the organization needs ML training pipelines reading the same data that powers BI; data volumes are in the petabyte range and storage cost is a meaningful budget line; multiple query engines serve different business units; the organization needs open format interoperability with partners; or AI agents need direct, governed access to historical data at scale. All of these requirements describe the conditions that the Agentic Lakehouse is purpose-built to address.
