Data lake or data warehouse: which one for your company?

A data warehouse stores structured, cleaned data ready for dashboards and reporting. A data lake stores large volumes of raw data in every format (text, files, streams, images) for advanced analytics and AI. If your priority is reliable business reporting, start with a data warehouse. If you need to exploit large and varied volumes, especially for AI, a data lake or a lakehouse architecture is a better fit.

The data warehouse in short

• Structured data, organised according to a predefined model.
• High quality: data is cleaned before it is loaded.
• Main use: reporting, dashboards, financial and sales analysis.
• Users: management, controllers, business analysts.

The data lake in short

• Every data format, including unstructured: documents, logs, social media, sensors.
• Raw storage, with structure applied at analysis time.
• Main use: data science, machine learning, text analysis, real-time processing.
• Users: data scientists and data engineers.

The lakehouse: the best of both?

The lakehouse combines the flexible, cost-effective storage of a data lake with the reliability and query performance of a data warehouse. It feeds both dashboards and AI projects from a single platform, and is now the preferred architecture for many new projects.

Criteria to choose

1. Your priority use cases: reporting or AI?
2. Data types: only tables from your software, or also text, files and streams?
3. Current and expected volumes.
4. Skills available in the team.
5. Real-time needs.
6. Hosting and data sovereignty constraints.

A real example

For Afreximbank, STEPS designed a data lake that continuously collects content from more than 100 websites and external sources, cleans and structures it through real-time pipelines, then analyses it with natural language processing to compute a sentiment index. This kind of project, combining large volumes, unstructured text and AI, is exactly where a data lake makes sense.

Common mistakes

• Building a data lake without governance, which quickly becomes an unusable data swamp.
• Launching a full platform before a first use case creates value.
• Underestimating data quality at the source.

How STEPS helps

STEPS designs data and AI engineering architectures adapted to each company’s size and goals, up to the dashboards and AI models that use them. To define the right architecture for your case, book a scoping session.

Frequently asked questions