Data Lake vs Data Warehouse vs Data Lakehouse: How They Differ and When to Use Each
Last updated: 2026-06-09 · By Vector Search Engineering, Zilliz
Quick answer. In the data lake vs data warehouse comparison, a data warehouse stores structured, processed data with schema enforced on write — built for SQL and business intelligence. A data lake stores raw data in open files on cheap object storage with schema applied on read — built for machine learning and exploration. A data lakehouse merges the two, adding warehouse-grade management (ACID transactions, schema) to lake storage through open table formats such as Apache Iceberg and Delta Lake. The deciding factor is your workload: governed SQL reporting, raw-data flexibility, or both on one copy of data.
What is a data warehouse, a data lake, and a data lakehouse
Data warehouse. A centralized repository for structured, cleaned data, optimized for SQL analytics and reporting. Schema is defined before data is loaded (schema-on-write), which enforces consistency but makes change costly. Mature platforms include Snowflake, Amazon Redshift, Google BigQuery, and Teradata. Warehouses deliver fast, governed BI; modern cloud warehouses decouple storage from compute, though both still run on the vendor's proprietary engine and managed storage format.
Data lake. A centralized store that holds raw data of any shape — structured, semi-structured, and unstructured — as open files on object storage such as Amazon S3, Azure Data Lake Storage, or Google Cloud Storage. Data lands first and schema is applied at read time (schema-on-read), using engines like Apache Spark or Trino. File formats include Apache Parquet, ORC, Avro, and JSON. Lakes are cheap and flexible but, without discipline, degrade into an ungoverned "data swamp."
Data lakehouse. An architecture that puts warehouse-style management on top of lake storage. Open table formats — Apache Iceberg, Delta Lake, and Apache Hudi — add ACID transactions, schema evolution, and time travel over the same open files on object storage, so one copy of data serves both BI and ML. The term was popularized around 2020; Delta Lake was open-sourced in 2019, and engines such as Apache Spark, Trino, and Presto query lakehouse tables directly.
Key Differences
The three architectures differ less in where they store data than in how much structure and governance they impose on it.
| Dimension | Data Warehouse | Data Lake | Data Lakehouse |
|---|---|---|---|
| Data shape | Structured, processed | Raw — any structure | Raw + structured |
| Schema | Schema-on-write | Schema-on-read | Enforced via table format |
| Storage | Managed columnar store | Open files on object storage | Open table formats on object storage |
| Compute model | Decoupled (modern cloud), proprietary engine | Decoupled, bring-your-own engine | Decoupled, open engines on open tables |
| Transactions | Full ACID | None / weak | ACID via table format |
| Primary workload | BI, SQL reporting | Data science, ML, batch | BI + ML on one copy |
| Cost profile | Higher per TB | Lowest per TB | Low storage + elastic compute |
| Governance | Mature, built-in | "Data swamp" risk | Restored via table-format metadata |
| AI / vector readiness | Native vector types (Snowflake, BigQuery), served on general-purpose warehouse compute | Vectors as raw files, no index | Iceberg / Delta lack a standard ANN index; Lance and Vortex are vector-oriented but emerging |
The progression is historical, not just architectural. Warehouses came first, formalized in the 1990s around governed SQL analytics. Data lakes rose with Hadoop in the late 2000s, trading governance for the economics of commodity object storage and the freedom to keep raw, multimodal data. The lakehouse is the synthesis: open table formats re-introduced the transactions, schema enforcement, and metadata that lakes gave up, without giving back the low cost or open access. That is why the modern question is rarely "lake or warehouse?" but "do I need a separate warehouse at all, or can one governed lakehouse serve both?"
One axis the table makes explicit is AI readiness. Warehouses have recently added native vector types — Snowflake's VECTOR type, BigQuery's vector indexes and VECTOR_SEARCH — but each runs on that warehouse's own general-purpose compute engine, so vector serving is tied to — and priced by — the warehouse rather than elastic, index-specialized infrastructure. The lake side is more uneven: Apache Iceberg and Delta Lake have no standard approximate-nearest-neighbor (ANN) index, while newer columnar formats designed with vectors in mind (Lance, Vortex) are emerging but not yet a standard part of the lakehouse stack. Storing an embedding column is easy; serving it at production latency is not. For 1B 768-dim vectors, the raw float32 data alone is about 3 TB, and even compressed the index stays large — so because object stores answer each read in roughly 20-50 ms (far slower than RAM), a brute-force similarity scan over lake files takes minutes to hours, not the milliseconds production search needs. Indexed engines cut that by pruning each query to around 3% of the vectors, but most lake formats provide no such index of their own. So teams building retrieval or semantic search still bolt a separate vector database (Pinecone, Weaviate, Qdrant) onto the side — reopening the copy-and-sync problem the lakehouse was meant to close.
When to Use Each
Choose a data warehouse when your workloads are predominantly structured SQL and BI: dashboards, financial reporting, well-defined schemas, and strict governance where mature ACID guarantees and query performance justify a higher per-TB cost.
Choose a data lake when you store large volumes of raw, semi-structured, or multimodal data for data science and machine learning; your schema is still evolving; you are cost-sensitive at scale; and you prefer to bring your own engines (Apache Spark, Ray, Trino) over the files.
Choose a data lakehouse when you want both BI and ML on a single governed copy of data without maintaining two separate stacks. It fits teams that need ACID transactions and schema enforcement but refuse to give up open formats, low object-storage cost, or engine choice — and want to avoid duplicating data between a lake and a warehouse.
If your roadmap includes retrieval-augmented generation, recommendation, or semantic search, weigh a fourth factor: warehouses' native vector types are scoped to their own compute, and lake table formats still lack a standard ANN index — so plan for how embeddings will be indexed and served alongside this data.
How Vector Lakebase Approaches This
The lakehouse collapsed the lake/warehouse divide for tabular and ML data on one copy — but its table formats (Iceberg, Delta Lake, Hudi) were built for columnar scans, not vector retrieval, so AI teams still copy embeddings into a separate vector database. Zilliz Vector Lakebase extends the lakehouse pattern to semantic data through Unified Lake-Native Storage: embeddings and their source documents live in the same Iceberg / Lance / Parquet / Vortex tables on object storage and are indexed in place, rather than mirrored into a second system. Note that this is a different product from Databricks Lakebase, which unifies online transaction processing (OLTP) and analytics (OLAP) on Postgres and Delta Lake; the two are complementary, not competitive, and many teams will use both.
Frequently asked questions
Is a data lakehouse just a data lake with extra features? Functionally, yes — but the extras are what make it usable for analytics. A lakehouse keeps the lake's open files on object storage, then layers a table format (Iceberg, Delta Lake, or Hudi) on top to add ACID transactions, schema enforcement, and time travel. That metadata layer is what prevents the "data swamp" problem and lets BI tools query the lake reliably.
What is the difference between schema-on-read and schema-on-write? Schema-on-write (warehouses) validates and structures data before it is stored, so reads are fast and consistent but ingestion is rigid. Schema-on-read (lakes) stores data raw and applies structure when it is queried, so ingestion is flexible and cheap but query-time interpretation can vary. Lakehouses blend both, enforcing schema through the table format while keeping open storage.
Do I still need a data warehouse if I have a lakehouse? Often not. A lakehouse is designed to serve BI and ML from one copy of data, which can replace a separate warehouse for many teams. Some organizations still keep a warehouse for highly tuned, low-latency reporting or existing tooling, but the trend is consolidation onto a single governed lakehouse to avoid duplicating data and pipelines.
Which is cheaper, a data lake or a data warehouse? A data lake is usually cheaper per terabyte because it stores open files on commodity object storage and decouples compute, so you pay for query engines only when they run. Warehouses cost more per TB, partly from managed storage and historically coupled compute, in exchange for performance and governance. A lakehouse aims for lake-level storage cost with warehouse-level management.
Can a data lakehouse handle AI and vector search workloads? Not yet, on its own. Open table formats like Iceberg and Delta Lake can store embedding arrays as a column, but they have no built-in ANN index, so similarity search over them is a brute-force scan that takes minutes to hours at scale. Newer lake-oriented formats like Lance and Vortex are designed for vector access patterns, but the index layer is still emerging. To serve real-time vector search today you need an index built over those embeddings — either in a separate vector database or by an engine that indexes the lake files in place.
Related reading
- data lakehouse architecture explained — the deeper pillar on the lakehouse model
- data lakes in the AI era — how lakes evolved for multimodal and ML data
- can you search a data lake without moving data? — the vector-search angle on lake storage
Bottom line. A data warehouse governs structured data for SQL and BI; a data lake stores raw data cheaply on object storage for ML; a data lakehouse fuses both, adding ACID and schema to open lake files so one copy serves both. Pick by workload — and if AI retrieval is on the roadmap, plan separately for how vectors get indexed. See how this architecture plays out in practice in the Vector Lakebase launch overview, or start free with $100 in credits.