Data lake

What is a data lake?

A data lake is a store that holds raw data in its original form, at almost any scale, without forcing a structure on it first. Structured tables, JSON logs, images, sensor readings, and clickstream events can all sit in the same lake. You decide how to shape the data when you read it, not when you write it. That flexibility is the whole point.

This is the core difference between a data lake and a data warehouse. A warehouse demands structure up front and rewards you with fast, governed queries. A lake takes anything and defers the structure, which keeps options open but puts more work on whoever later tries to make sense of it. A media company might dump every raw event from its apps into a lake, then transform a slice of it into clean tables only when a specific analysis or model needs it. Lakes usually sit on cheap object storage like Amazon S3, which is what makes "keep everything" affordable.

Lakes and warehouses often coexist. Many companies land raw data in a lake, then push refined, trusted subsets into a warehouse for reporting. The newer "lakehouse" pattern tries to merge both, adding warehouse-style structure and governance on top of lake storage.

Data lakes at Dallonses

We build data lakes for clients who genuinely need to keep everything, teams training models, running heavy analysis, or working with data too varied to fit neat tables. A lake without discipline turns into a swamp, so we put cataloging, access control, and structure where it counts from the start.

Most of the time a lake is one piece of a larger picture. We pair it with a warehouse so raw data and trusted reporting each have a proper home, and we make the boundary between them clear. Real data analytics needs both the freedom to explore raw data and the discipline to report clean numbers, and we design the architecture so a client gets both without the mess.

Sitting on raw data you can't yet use? Let's turn it into something you can.

Talk to us about data