Data Lakes and Data Warehouses Choosing Your Path

Data teams often face a familiar choice: build with a data lake or a data warehouse. A data lake stores data in its raw form and handles many formats, from logs to images. A data warehouse stores cleaned, structured data designed for fast, reliable queries. Both have strengths and limits, and the best solution today often uses both, or a lakehouse that blends features. It helps to see how teams use each option in practice.

What they do well

  • Data lake: flexible storage, scalable, and usually cheaper per byte. It handles diverse data like text, logs, and media.
  • Data warehouse: fast query performance, strong governance, and stable datasets for dashboards and reports.

Choosing your path

Consider who will use the data and how it will be used. If you want exploratory work, machine learning, or you ingest many data formats, start with a data lake. If you need reliable dashboards, regulated reporting, and clear data quality, a data warehouse helps. For many teams, a lakehouse or a layered approach gives the best balance.

Practical steps

  • Map sources, users, and typical queries to guide design
  • Start with a small core dataset for dashboards and a simple data catalog
  • Use ELT: load raw data first, then transform for analysis
  • Establish governance, metadata, and lineage from day one
  • Pilot with a concrete use case and measure value before expanding

A practical example

A retailer collects website logs, sales transactions, and product data. Logs and unstructured data live in the data lake. Core sales tables go to the warehouse for fast BI. ML models can use lake data with governance in place. Over time, a lakehouse pattern can unify access and keep governance consistent.

Conclusion

Choosing a path is not a single decision. Many teams benefit from combining data lakes, warehouses, and clear governance. Start small, learn from usage, and keep data accessible and well organized.

Key Takeaways

  • Start with a clear view of who uses the data and for what purpose
  • Use ELT to separate raw ingestion from planning and analytics
  • Consider a lakehouse or layered approach for balance and flexibility
  • Invest in governance and metadata to maintain trust
  • Balance cost, latency, and data variety when designing your architecture