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What is Data Lake?
A storage system that holds large volumes of raw data in its original format – structured spreadsheets, unstructured documents, images, logs – until you need to analyse it.
What it is
A data lake is a big bucket where you store everything – sales records, emails, PDFs, sensor readings, website clickstreams – without needing to decide how to structure it first. If a data warehouse is a library with every book catalogued and shelved, a data lake is a storage unit where you can throw everything in and organise it later. The advantage is flexibility: you keep data that might be valuable in the future without the upfront cost of cleaning and structuring it. Cloud platforms like AWS S3, Azure Data Lake, or Google Cloud Storage make this cheap – often pennies per gigabyte per month.
Why it matters for your business
Data lakes become relevant when you have data that does not fit neatly into rows and columns – documents, images, IoT sensor data – or when you need to keep raw data for compliance or future analysis. A $120M manufacturing client kept two years of machine sensor data in a lake at a cost of $60 per month. When they later built a predictive maintenance model, that historical data was the difference between a model that worked and one that guessed. That said, most mid-market companies should start with a warehouse, not a lake. Lakes without governance become "data swamps" – dumping grounds nobody can navigate.
How we approach it
We recommend data lakes when there is a clear use case: compliance archives, unstructured data for ML, or high-volume event data. We set them up with governance from day one – folder structures, naming conventions, access controls, and metadata catalogues. When clients need both structured reporting and raw storage, we build a "lakehouse" pattern: raw data lands in the lake, gets cleaned and promoted to the warehouse layer for business use.
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Key Takeaways
- •A data lake stores raw data in any format cheaply – but without governance it becomes a swamp nobody can use.
- •Most mid-market companies should start with a warehouse for structured reporting and add a lake later if needed.
- •Cloud storage makes lakes surprisingly affordable – often $50–100 per month for terabytes of historical data.
- •The best pattern for growing companies is a "lakehouse" – raw data in the lake, clean data promoted to the warehouse.