Data Lake vs Data Warehouse vs Lakehouse
Basics4 minInnovialabs editorial team
In short
A data warehouse stores cleaned, structured data for reporting, a data lake stores any raw data cheaply in its original form, and a lakehouse adds warehouse-style tables and transactions on top of a lake. Many small companies only need a regular database and a BI tool.
The three approaches in short
- Data warehouse — structured tables with a predefined schema. Data is cleaned and consistent, and SQL reports run fast.
- Data lake — cheap object storage (S3, Google Cloud Storage, Azure Data Lake Storage) where everything lands in its original form: database dumps, logs, API JSON, files, images. The schema is applied when reading.
- Lakehouse — a data lake plus an open table format (Delta Lake, Apache Iceberg, Apache Hudi) that gives files in the lake transactions, versions and a schema. The idea is to combine the low cost of a lake with the reliability of a warehouse.
Comparison
| Aspect | Data warehouse | Data lake | Lakehouse |
|---|---|---|---|
| Structure | Strict schema on write | Any format, schema on read | Tables on top of lake files |
| Data types | Tables | Tables, logs, JSON, text, media | All, but analytics mostly on tables |
| Storage cost | Higher, billing often tied to compute | Low, object storage | Low, same as a lake |
| Main users | Analysts, BI, management | Data engineers, data scientists | Analysts, engineers and ML teams |
| Typical use | Reports, dashboards, financial metrics | Raw data archive, ML, log processing | BI and ML on the same data without copies |
| Examples | BigQuery, Snowflake, ClickHouse | S3, GCS, ADLS | Databricks, Iceberg tables in various engines |
| Main risk | Costly as raw data volumes grow | A "data swamp" with no catalog or owners | Complexity to adopt and maintain |
What fits when
Warehouse — when reports and dashboards are the main consumers, sources are mostly tabular (CRM, accounting, website database), and the team values simple SQL and predictable numbers.
Lake — when you have lots of unstructured or semi-structured data: logs, events, files, text; you want to store everything cheaply "for later"; you have machine learning tasks.
Lakehouse — when data is already large and varied, and running both a lake and a warehouse with copying between them has become expensive and complex. It is a solution for a mature data team, not a starting point.
In practice a common setup is both: raw data in the lake, a cleaned subset loaded into the warehouse for BI.
Common mistakes
- A lake with no order. Files pile up without descriptions, owners or retention rules. A year later nobody knows what is inside, and the lake becomes a swamp.
- Choosing by hype. A lakehouse gets built where a single analytical database would do.
- No shared definitions. No architecture helps if "revenue" is calculated differently in different reports.
- Uncontrolled personal data. Raw exports easily end up containing phone numbers and ID data with access open far too widely.
When you need none of them
A small or mid-sized company is often too early for a dedicated data architecture. Signs:
- only a few data sources, for example a CRM, an accounting system and a website;
- the data fits in a regular database and queries are fast;
- reports are needed daily or weekly, not in real time;
- no machine learning work and no large event streams.
What to do instead:
- Set up a read replica of the production database or a separate analytics schema in PostgreSQL.
- Connect a BI tool (Metabase, Looker Studio, Power BI) to that replica.
- Write down 5–10 key metrics and their definitions.
- Revisit the decision when queries slow down or new sources appear.