Data lake or data warehouse: which one your company needs
A warehouse holds cleaned data built for reporting. A lake holds raw data of every kind for whoever needs it later. Most companies end up needing both.
Data warehouses and data lakes both hold company data for analysis. They differ in when the data gets structured, and that one difference drives who can use them, what they cost to run and how much they can be trusted.
The difference in one table
| Data warehouse | Data lake | |
|---|---|---|
| What goes in | Structured data from business systems: ERP, CRM, finance, sales | Any data: structured tables, logs, documents, images, IoT and app events |
| When it is structured | Before it is loaded. The schema is designed up front | When it is read. Data is stored as it arrives |
| Data quality | Cleaned and checked, treated as the trusted source | Raw, and quality varies |
| Main users | Business analysts and reporting teams | Data scientists, data engineers and analysts |
| Typical use | Dashboards, financial reporting, standard KPIs | Machine learning, exploration, keeping data you might need later |
AWS describes a data lake as a central repository that lets you store structured and unstructured data at any scale, without structuring it first. A warehouse, by contrast, defines its structure in advance so SQL queries run fast and results can be trusted for reporting.
When a warehouse is the right call
If most of your analytics is reporting on business systems, a warehouse is the simpler choice. Finance wants revenue by region, sales wants pipeline by quarter, and both need the numbers to match. The up-front modeling work is what makes that consistency possible.
Modern cloud warehouses also take semi-structured data such as JSON, so the line has blurred. But the discipline stays the same: data is modeled before people rely on it.
When a lake earns its place
A lake makes sense when you collect data that doesn't fit neatly into tables, or that you will only know how to use later. Application logs, clickstream events, sensor data, documents for AI search and training data for machine learning all fit here.
The risk with a lake is that it fills up with data nobody understands. Without a catalog, owners and access rules, a data lake turns into a data swamp: lots of storage cost, little trust.
Lakehouses: the middle path
A lakehouse stores data in open formats on low-cost object storage, like a lake, and adds the table structure, transactions and governance of a warehouse on top. Platforms from Databricks, Snowflake and the large cloud providers now market some version of this.
For a mid-size company, the appeal is one copy of the data serving both reporting and data science. The catch is that you still need the modeling and governance work. A lakehouse removes the duplicate storage, not the design.
How to choose
Start from the questions people ask today. If they are mostly reporting questions about business systems, start with a warehouse. If you are building AI features or collecting large volumes of raw event data, plan for a lake or lakehouse alongside it. AWS itself notes that a typical organization often needs both.
Whatever you choose, assign an owner to each important dataset and write down where it comes from. That does more for data quality than any platform choice.
What IT teams should do
- List the top ten questions the business asks of its data, and where each answer comes from today.
- Name an owner for each core dataset: customers, products, revenue.
- If you already run a lake, check that it has a catalog and access controls.