Data Lake vs. Data Warehouse: What’s the Difference?

[shukla7789]

Clock Icon
7 min. read
Abby Fields
Abby Fields
Verified badge
Branded Content Specialist
Chevron Down Icon
Data lakes and data warehouses both help you effectively collect and store big data. But while the two terms sound similar, they have several differences. A data lake is a collection of raw, unorganized data. A data warehouse collects, stores, and filters processed data for easy analysis.

These two terms are often used interchangeably, but have many overseas chinese in canada data and uses that can help you maximize your marketing campaigns with data.

That’s why we’ll dive into the key differences between a data lake vs. data warehouse on this page. So keep reading to learn more!

Bonus: Want to learn even more about data storage and data-driven marketing? Then join over 200,000 marketers who get the latest data-driven marketing advice and tips from our experts by signing up for our newsletter, Revenue Weekly!

Defining a data lake vs. data warehouse
Before we dive into what makes data warehouses and data lake different, let’s define each term below:

What is a data lake?
A data lake is a system that allows you to store all of your data at any scale. Data lakes can help you collect unorganized, raw data in any size that you can analyze later.

Think of data lakes as actual bodies of water. You can store a vast amount of data in the data lake that floats around until you or another team member dive in to examine or analyze it.

What is a data warehouse?
A data warehouse is a system that allows you to store, manage, and analyze data. Data warehouses use sales dashboards, reports, and other analytics tools to help you organize and interpret your data.