Data Warehouse vs Data Lake vs Lakehouse: Choosing a Data Architecture
Data warehouse vs data lake vs lakehouse: how they differ on data types, cost, governance and skills, open table formats, and which fits your business.
Warehouse, lake or lakehouse?
A data warehouse stores structured, modelled data for fast SQL reporting and suits most business analytics. A data lake stores raw data of any type cheaply for later processing and suits large, varied or unstructured data. A lakehouse adds warehouse-like tables, transactions and governance on top of lake storage using open table formats, serving analytics and data science from one copy. Most growing businesses should start with a warehouse. Choose based on your data types, workloads and skills.
This guide is for technical leads and data owners. For building the pipelines, see data engineering for growing businesses; for online stores, ecommerce data warehouses.
Key takeaways
- Warehouses are the simplest route to trusted reporting.
- Lakes store raw and unstructured data cheaply but need discipline.
- Lakehouses combine both using open table formats.
- Skills and governance matter more than the label.
- Check data location for storage, compute and backups.
Comparison
| Factor | Data warehouse | Data lake | Lakehouse |
|---|---|---|---|
| Data types | Structured, modelled | Any: tables, files, logs, documents, media | Any, with tables over files |
| Schema | Defined on write | Applied on read | Managed table schemas with evolution |
| Main users | Analysts, BI, finance | Data engineers, data scientists | Both |
| Query performance | Strong for SQL analytics | Depends on engine and layout | Good with well-managed tables |
| Governance | Built in | Easy to lose track (the 'data swamp') | Table-level controls and catalogues |
| Skills | SQL and modelling | Engineering and file formats | Engineering plus SQL |
| Lock-in | Engine-specific storage | Open files | Open formats, several engines |
Open table formats
Open table formats such as Apache Iceberg and Delta Lake add transactions, schema evolution, time travel and partition management to files on object storage, so several query engines can work on the same tables. Many warehouses can now read or manage these formats too, which blurs the line between warehouse and lakehouse. The practical question is which engines and tools your team will use.
Which fits your business?
- Warehouse: reporting from ecommerce, accounting, CRM and product databases; a small team with SQL skills.
- Lake: large volumes of logs, sensor data, documents or media to store before you know how you will use them.
- Lakehouse: both BI and machine learning on large, varied data; a desire to avoid locking data into one engine; a team that can run it.
- Start simple: a warehouse now, with raw data kept, rarely blocks a later move.
Cost, skills and data location
Compare total cost for your real workloads: storage, compute, data movement, tools and engineering time. Warehouses trade higher per-query or compute costs for less engineering; lakes and lakehouses trade cheaper storage for more engineering. Set budgets and monitoring from day one; see cloud cost optimisation. Choose Australian regions where your obligations require it and confirm where backups and support access sit.
Hypothetical example: a manufacturer
This example is hypothetical. A manufacturer wanted sales and margin reporting and, later, analysis of machine sensor data. It started with a cloud warehouse for ERP, CRM and ecommerce data. A year later, sensor data volumes grew large, so it stored raw sensor files in object storage as Iceberg tables in an Australian region, queried from the same warehouse engine for summaries and by data scientists for modelling. Business reporting did not change.
Common mistakes
- Building a lake with no catalogue, ownership or quality checks.
- Choosing a lakehouse for prestige without the skills to run it.
- Discarding raw data, so models cannot be rebuilt.
- Comparing storage prices but not compute and engineering.
- Forgetting data location for backups and replicas.
Related guides
- AI data engineering for data used by AI applications.
- Business intelligence dashboards for serving results.
- Predictive analytics vs generative AI for what to do with the data.
- Cloud migration for moving on-premises databases.
Conclusion
Warehouses, lakes and lakehouses solve overlapping problems. Most growing businesses get the most value from a warehouse first, keeping raw data so options stay open. Add lake storage or open table formats when data volume, variety or machine learning needs justify the extra engineering.
Choosing a data platform?
ZSpace Labs is an India-based, remote-first technology studio working with Australian and international businesses on data integrations and analytics platforms and AI on business data. If it would help to compare options for your data, we are happy to talk it through.
Common questions.
A data warehouse stores structured, modelled data optimised for SQL analysis and reporting. A data lake stores large volumes of raw data in many formats, including files, logs, images and documents, usually in low-cost object storage, and structure is applied when the data is read.