A vendor-neutral, head-to-head comparison of enterprise data warehouses, data lakes, and lakehouses - with a clear view of the BI, governance, and latency workloads where the EDW still wins in 2026.
A warehouse migration can lower storage costs and still be a poor investment if it forces a team to revalidate years of financial reports. A warehouse can also be the wrong place to keep raw training data that several engines need to process. Those are different workloads, and they need not share one answer.
The useful comparison is between the services each architecture provides, the work your team must do, and the data contracts your consumers rely on.
What the three terms mean
An enterprise data warehouse (EDW) organizes analytical data for shared reporting and business intelligence. Its value comes from consistent models, controlled access and dependable queries. It can run on premises or as a cloud service. Modern cloud warehouses commonly separate storage and compute; tightly coupled storage is not a defining EDW property. Snowflake's architecture is one example.
A data lake stores datasets in object storage or another shared storage system. It can contain raw files, curated datasets, images and other objects. Schema-on-read is common, but a lake can still have ingestion validation, access controls and a catalog. Raw-file archives remain a valid use case; not every file belongs in a transactional table.
A lakehouse adds table management and analytical serving over that storage. Formats such as Iceberg, Delta Lake and Hudi supply transaction and metadata mechanisms, while query engines and catalogs supply execution and governance. Iceberg's documentation describes its snapshot and evolution features. These are table-level capabilities; they do not automatically give every engine identical SQL behavior or cross-table transaction semantics.
| Question | EDW | Data lake | Lakehouse |
|---|---|---|---|
| What is the organizing unit? | Governed analytical tables and models | Files and datasets | Tables backed by files and transaction metadata |
| Who provides SQL execution? | The warehouse service or database | A separate query engine | One or more compatible engines |
| What needs ongoing maintenance? | Models, access, workload sizing and service configuration | Layout, cataloging, retention and quality | Those responsibilities plus table metadata, compaction and reader/writer compatibility |
| How does it support ML? | Built-in services or external integrations, depending on product | Direct access to raw datasets | Table snapshots accessible to compatible processing tools |
| What determines latency? | Data layout, workload isolation, compute and query shape | Engine, file layout and amount scanned | Engine, layout, maintenance and workload isolation |
| What determines cost? | Compute, storage, features and operations | Storage, requests, transfer, compute and operations | The same, plus catalog and maintenance work |
No row in this table establishes a universal price or performance winner.
When keeping the warehouse makes sense
The reports already work and their semantics matter. Reproducing a certified revenue report requires more than matching row counts. Currency conversion, late adjustments, timezones, historical dimension rules and access policies all need to agree. A cheaper scan engine may not repay the migration effort.
The team needs an integrated operating model. A managed warehouse can package workload management, permissions, audit trails and familiar SQL tooling. It still needs configuration and data ownership, but there may be fewer components for a small team to operate.
Concurrent BI is the main workload. Separate compute pools can protect reporting from batch jobs. Snowflake virtual warehouses, BigQuery reservations and Redshift workload management provide different forms of isolation. Lakehouse SQL services can do this too; benchmark your dashboard workload rather than assuming an architectural label guarantees sub-second response.
Existing controls are expensive to rebuild. Access reviews, retention, lineage and change approval may be embedded in the current platform. Preserve those controls during any move. Neither schema-on-write nor a particular database product is, by itself, evidence of regulatory compliance.
These are reasons to retain a working system, not reasons to exclude all new data or ML workloads from a warehouse. Cloud warehouses support semi-structured data and ML integrations as well as BI.
When a lakehouse is worth the work
A lakehouse is attractive when several engines need access to large, shared analytical tables, when open storage interfaces matter, or when long retention makes a separate copy in every service costly.
For example, Spark might build a training dataset while Trino runs exploratory SQL over the same Iceberg snapshot. Sharing files can avoid an export, provided both readers support the table's format version, types and delete representation. A snapshot must remain retained for reproducibility; time travel cannot recover data that maintenance has already removed.
The Iceberg reliability model uses optimistic concurrency for compatible commits. A shared catalog coordinates table state. It does not make incompatible writers safe, and a failed validation may require an operation to be retried or redesigned.
Open formats reduce one form of lock-in. They do not make SQL dialects, views, row filters, IAM policies or catalog grants portable without work. Test a second reader and a recovery scenario before treating portability as a delivered feature.
Governance needs an enforcement map
For each data path, identify who authenticates the user, who authorizes table access, who applies row or column restrictions, and who can read the underlying objects directly. A catalog policy is ineffective if users can bypass it with broad storage credentials.
The Iceberg REST catalog protocol standardizes catalog interactions. Apache Polaris is one implementation, and it graduated to an Apache top-level project in February 2026. That shared interface helps interoperability; it does not imply that every client supports the same fine-grained enforcement.
This is also where a metadata catalog and a transactional table catalog differ. A searchable lineage graph can explain where data came from without being the component that authorizes a query or commits a write.
A hybrid can be deliberate
One practical design retains trusted BI tables in a warehouse, lands large histories in open tables, and materializes selected results into a serving store when measured latency requires it. For the ClickHouse option discussed here, use 26.8 LTS and distinguish direct Iceberg reads from loading data into native MergeTree tables. The latter is another maintained copy, with its own freshness and reconciliation requirements.
A hybrid is useful only if ownership is clear. Define the authoritative source for each business fact, the acceptable lag of derived stores, and how deletes and corrections propagate. A single catalog or semantic layer can help, but it cannot reconcile inconsistent business logic on its own.
Decide with a representative workload
- Select a reporting workload, a historical scan and an ML or batch workload that actually matter to the business.
- Compare their results and latency under realistic concurrency, including cold reads and maintenance activity.
- Include storage, compute, transfer, catalog, maintenance and engineering costs.
- Test recovery, retention and access enforcement through every reader.
- Migrate one dataset only after its consumers and rollback path are accounted for.
Keep the warehouse where its integrated service and established models earn their cost. Use a lakehouse where shared open tables solve a concrete access or retention problem. Our data warehouse architecture guide and data lake architecture guide cover the implementation choices behind that decision.
