Data Lakehouses: Combining Lake and Warehouse

Data lakehouses blend the best parts of two older ideas: the data lake and the data warehouse. A data lake stores raw data in many formats, from log files to JSON to images. A data warehouse stores clean, shaped data ready for fast SQL queries. A lakehouse adds reliable transactions, governance, and a unified view on top of the lake. This makes data easier to access, while keeping the lake’s flexibility.

What makes a lakehouse different? It aims for clean data without moving everything into a separate warehouse. You can store many data types and still run consistent analytics, dashboards, and machine learning. The result is one source of truth that supports BI tools, data science, and experimentation alike.

How it helps teams:

  • Faster decisions with reliable query performance on large datasets.
  • Lower data duplication by avoiding copies between lake and warehouse.
  • Improved governance and lineage, so data users trust what they see.
  • Easier collaboration, as both engineers and analysts work from the same data.

A practical path to adoption:

  • Start by mapping your common analytics workloads and daily reports.
  • Choose a lakehouse platform or open formats like Delta Lake, Apache Iceberg, or Apache Hudi.
  • Add a light metadata catalog and basic data governance to track data sources and schemas.
  • Pilot with a simple product, such as web events or sales data, and build a few standard BI tables.

Common pitfalls include overcomplicating governance too early, ignoring cost control, or sticking to old ETL habits that slow you down. Plan for incremental growth: begin with a small, well-scoped data product, then broaden as teams gain confidence.

Example: store raw event logs in the lake, create clean user activity tables, and give analysts a fast SQL view for dashboards. Data science teams can pull feature data directly for model training, without moving data into a separate store every time.

By unifying storage and analytics, data lakehouses offer a practical path to faster insights and clearer data accountability.

Key Takeaways

  • Lakehouses combine flexibility with reliability for analytics.
  • Start small with governance and a single data product to learn fast.
  • Use open formats and catalogs to avoid vendor lock-in.
  • Align BI, analytics, and ML workloads on one trusted data source.