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What is a data lake / lakehouse?
A data lake is a central repository for large volumes of raw data—in its original format. Unlike a data warehouse, data is not structured in advance but is interpreted only when it is used (schema-on-read).
A lakehouse combines the flexibility of a data lake with the structure and consistency features of a data warehouse. Modern formats such as Delta Lake, Apache Iceberg, or Apache Hudi enable ACID transactions and schema evolution within the lake.
Typical platforms: Microsoft Fabric (with OneLake as the lakehouse foundation), Databricks (Delta Lakehouse concept), Snowflake (Data Cloud with lakehouse features), as well as native cloud offerings from AWS (S3 + Glue) and Google (BigQuery + Cloud Storage).
For midsize businesses, lakehouses are the logical evolution of the data warehouse—more flexible, more cost-effective, and AI-ready. Making the switch is almost always worthwhile.
Best Practices for Lakehouse Projects
These six principles make all the difference:
- Implement a medallion architecture: Bronze/Silver/Gold—clearly distinct quality tiers.
- Choose open formats: Delta or Iceberg — avoid vendor lock-in.
- Establish governance early on: roles, permissions, and lineage—don’t wait until later.
- Plan for streaming: Even if starting with batch processing — build a streaming-ready system.
- Cost control: Compute is scalable, but without control it quickly becomes expensive — set up alerts.
- Data Contracts: Clear contracts between data providers and consumers — explicitly define quality.
Common Mistakes in Lakehouse Projects
We frequently encounter these pitfalls:
- Data Swamp: Without governance, the lake becomes neglected—no one can find or trust the data.
- No Zones: Bronze/Silver/Gold aren’t separated—raw data ends up in reports.
- Fragmented Files: Millions of tiny files slow down every query—optimize regularly.
- Wrong Format: CSV or JSON instead of Parquet/Delta—poor performance and high costs.
- No metadata catalog: Without a catalog, there’s no discovery path—usage remains low.
Data Lake vs. Data Warehouse vs. Lakehouse
Three storage approaches with distinct strengths:
- Data Lake: Raw data in all formats. Flexible and cost-effective—but without structural guarantees.
- Data Warehouse: Structured data with ACID guarantees. Optimized for BI queries — more expensive, less flexible.
- Lakehouse: A combination of both worlds. Flexibility + structure—the modern standard.
Lakehouse for Your Data & AI
In a free initial consultation, we’ll take a look at your data landscape and determine whether and how a lakehouse can deliver the greatest value to you—including a platform recommendation.
As a data and AI partner for mid-sized businesses, we’ll build your Lakehouse in a pragmatic way—using Fabric, Databricks, or Snowflake—with a focus on business value.
What We Offer
- BI Consulting — The lakehouse as the foundation of your BI landscape.
- Software & Pipelines — Ingestion, transformation, and integration.
- AI Consulting — Machine learning on your lakehouse.
- Data Warehouse in the Glossary — a comparison with its classic predecessor.
