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What is the difference between a data lake and a data warehouse?
A data lake stores large volumes of raw data in many formats, structured or not, with little upfront organisation. A data warehouse stores cleaned, structured data modelled for reporting. Warehouses suit business reporting; lakes suit raw storage and data science, and most small companies only need a warehouse.
A lake offers flexibility but can become a swamp of undocumented files if poorly managed. A warehouse enforces structure, which makes analysis faster and more consistent.
Modern cloud platforms blur the line, and many setups keep a raw layer and a modelled layer in the same warehouse. For a small or mid-sized company, that single-platform approach is usually simpler and cheaper than running a separate lake.
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