Data Warehousing and Data Lakes for Analytics

Data analytics teams often work with two main data stores: data warehouses and data lakes. Each serves a different purpose, and together they form a practical architecture for analytics.

A data warehouse is a structured, optimized store designed for fast queries, dashboards, and consistent reporting. A data lake holds raw data in various formats, ready for exploration, experimentation, and advanced analytics. Those formats can be logs, CSV, JSON, images, or video. You can query them with flexible engines, run notebooks, or train ML models. Good governance, clear metadata, and solid security are essential for both.

What each store does

  • Data warehouse: curated, schema-on-write, strong data quality, repeatable reports, and fast BI.
  • Data lake: raw data, flexible formats, supports data science, machine learning, and experimentation.

Choosing a hybrid approach Many teams use both. The lake accepts many data sources first. Then a subset is cleaned and modeled in a warehouse for wide sharing. Data catalogs and lineage help users find data and trust it. ELT, not just ETL, can move large data with scalable compute. This setup supports both executive dashboards and data science projects.

A simple architecture pattern

  • Ingest data into the lake from apps, logs, and devices.
  • Apply cleaning, enrichment, and modeling in stages.
  • Publish curated data to the warehouse for dashboards and business metrics.
  • Create data marts for marketing, sales, or operations to speed use.
  • Add strong security and access controls to protect sensitive data.

Example scenario A retail company keeps click logs, product feeds, and sales records in a data lake. A weekly extract then feeds a data warehouse, where dashboards track revenue, margins, and stock. Analysts drill into lake data for experiments, model results, and new insights. In the cloud, managed services help scale storage and compute as data grows.

Best practices

  • Start with business questions, not tools.
  • Use a metadata catalog and data lineage.
  • Enforce data quality and access controls.
  • Plan for cost, scale, and automated testing.

Key Takeaways

  • A hybrid stance often works best: lake for raw data, warehouse for curated analytics.
  • Clear governance and metadata help every user.
  • Align data design with business questions and measures.