Data Lakes versus Data Marts: Tradeoffs
Data lakes and data marts are two common patterns for organizing data in modern teams. A data lake is a broad, scalable store for raw data from many sources. A data mart is a smaller, focused store that holds curated data for a specific business area or team. The key difference is how much processing happens before the data is used: lakes favor flexibility, marts favor speed and simplicity.
In practice, many teams start with a lake to capture everything and later build marts or a data warehouse to meet reporting needs. A lake uses schema-on-read, so analysts define the structure when they query. A mart uses schema-on-write, with cleaned data ready for BI tools. This often makes marts faster to analyze, but less adaptable to new data sources.
Common tradeoffs include cost, governance, and agility. Lakes scale cheaply for large volumes but require strong metadata and governance to keep data useful. Marts offer predictable performance and clear ownership, yet can create data silos if not aligned with enterprise standards. To bridge the gap, some organizations adopt a lakehouse approach or maintain a lake with curated marts on top. A good metadata catalog, lineage, and clear data ownership help every setup stay usable over time.
If you manage data for multiple teams, start by asking: How quickly do teams need results? How complex are the data sources? Who will govern the data? What level of quality is acceptable for decisions? By answering these questions, you can choose a pattern, or combine them, so data remains accessible and trustworthy.
Example: a retail company collects raw event logs in a data lake for data science and experimentation. At the same time, the marketing and finance teams rely on a data mart that contains cleaned sales, customer, and campaign data, optimized for dashboards. The organization can then layer a lakehouse to unify access while keeping specialized marts for speed.
Key steps to decide
- Define critical use cases and latency requirements.
- Assess data sources and cleanliness needs.
- Plan for metadata, cataloging, and data lineage.
- Align on governance and security across data stores.
- Consider a gradual path to a lakehouse if needed.
A thoughtful mix of data lakes, data marts, and governed data access helps teams stay flexible while delivering reliable insights.
Key Takeaways
- Choose data lakes for flexibility and large-scale collection; use data marts for fast, business-focused analysis.
- Governance, metadata, and lineage are essential to keep either approach usable.
- A lakehouse or a layered architecture can combine the strengths of both patterns.