Data Warehouse vs Data Lake vs Lakehouse: The Differences, With a Marketing Data Example

A data warehouse stores data that is already organised to answer business questions: tables with fixed columns, agreed definitions and SQL queries. A data lake stores raw data in whatever format it arrives in (tables, JSON, CSV, event logs, documents), on cheap storage, and gives it shape later, when someone needs it. A lakehouse is a mix of the two: the files stay in the lake, but there are tables on top with the guarantees of a warehouse.

If you are not yet sure what a data warehouse is or whether you need one, start with what is a data warehouse and when do you need one. Here we take that as read and move on to the differences, an example for marketing and sales, and which product is which.

For years, many companies ran both: a lake to keep everything and, next to it, a warehouse holding a copy of whatever the reports used. Two places, two copies and two bills.

The lakehouse removes that copy. Data stays in Parquet files in your cloud storage, and an open table format such as Delta Lake or Apache Iceberg adds what those files lacked to behave like real tables: transactions, schemas and queryable previous versions. A catalogue on top handles permissions. The same data then serves both the Power BI report and the Python model.

A data mart is a part of the data warehouse prepared for one area. The marketing mart holds campaigns, spend, leads and attribution; the sales mart, opportunities, orders and quotas by rep. It is smaller, easier to understand and usually what people actually open.

It no longer needs to be a separate database: it is normally a schema inside the same warehouse, built on the same shared tables. What you want to avoid is marketing and sales each building their own mart with their own copy of the customers, which brings back numbers that never match.

Say you want customer acquisition cost by channel. Bronze holds daily spend from Google Ads and Meta Ads, GA4 sessions with their source, and CRM opportunities with their original source. In silver, each platform's campaigns are grouped into the same channels, dates move to one time zone and every won customer is linked to their first touch. Gold has one table by month and channel with spend, new customers and cost per customer, and that is what the dashboard reads.

If you later decide to count a channel differently, you change the rule in silver and recalculate everything from bronze, history included. The formula and its traps are in how to calculate CAC.

In a business intelligence project, the data warehouse is the part nobody sees. The dashboard is what management opens on Monday, but the numbers come from the gold layer, and if that layer is well built the dashboard is almost the easy part. Without a warehouse, every report does its own cleaning and two reports with the same metric end up showing two numbers. More in business intelligence consulting.

For most marketing and sales teams, a data warehouse is enough: their sources are tables (CRM, ads, web analytics, billing), queried in SQL or through a BI tool.

A lakehouse is worth considering when:

• You have a lot of event or semi-structured data: detailed browsing, product usage, application logs.
• You plan to train models in Python and want them to work on the same copy as the reports.
• You want to keep documents, call transcripts or emails to analyse them with AI.
• Your company already runs Databricks or Fabric in other areas and joining in is the sensible option.

The line has blurred, so the useful question is not which label to pick, but which sources you have, what you want to answer and who will maintain it. That is what we work on in data strategy.