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Data lake or lakehouse: choosing a foundation for your business data

A concise comparison of two popular data architectures, and the questions that help you choose between them.

1 min read

When data is scattered across many systems — sales software, accounting, CRM, spreadsheets — a business usually needs one central place to store, process and analyse it. Two architectures are commonly considered: the data lake and the lakehouse.

Data lake: flexible storage

A data lake stores data in its raw form — structured, semi-structured and unstructured — at a low storage cost. Its strength is flexibility and scalability; its weakness is that without governance, data quickly becomes hard to find, hard to trust and hard to use for reporting.

Lakehouse: flexibility with reliability

A lakehouse adds capabilities traditionally found in data warehouses on top of a data lake: clear schemas, consistent transactions, versioning and strong query performance. As a result, a single platform can serve both management reporting and advanced analytics or machine learning.

Data lake

  • Stores raw data in any format
  • Low storage cost, easy to scale
  • Needs extra tooling for reliable reporting
  • Good for storage and data exploration

Lakehouse

  • Raw data plus schemas and transactions
  • Versioning and data-quality controls
  • Reporting and AI on one platform
  • Good when data is a core business asset
Data lake and lakehouse at a glance

Don’t skip data governance

Whichever architecture you choose, a data platform is only useful if people trust the numbers. Be clear about who owns each data domain, naming conventions, access permissions and how data quality is checked on a regular basis.

Questions to guide your choice

  • Will the data mainly feed periodic reports, or analytics and AI?
  • Does your team already have the skills to operate a data platform?
  • How quickly is your data volume growing?
  • Must your current reporting tools stay, or can they change?
Start from how the data will be used, then choose the architecture and tools.

Fetek’s data engineering team supports businesses from assessing their current state and transforming data through to building data lake and lakehouse platforms and reporting systems, using technologies such as PostgreSQL, Databricks, Snowflake and Hadoop.

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