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

Data lakes and data warehouses are both storage systems for big data used by data scientists, data engineers, and business analysts. But while a data warehouse is designed to be queried and analyzed, a data lake (much like a lake filled with water) has multiple sources (tributaries or rivers) of structured and unstructured data flowing into one combined site.

The two storage systems serve different purposes, so different job roles work with each of them. For some companies, a data lake works best, especially those that benefit from raw data for machine learning. For others, a data warehouse is a much better fit because their business analysts need to decipher analytics in a structured system.

Read more: What Is a Data Scientist? Salary, Skills, and How to Become One

Read on to learn the key differences between a data lake and a data warehouse.

Data lake vs data warehouse: Key differences

The key differences between a data lake and a data warehouse are as follows [1, 2]:

To gain a deeper introduction to data lakes, check out this video from Google:

What is a data lake?

A data lake is a storage repository designed to capture and store a large amount of structured, semi-structured, and unstructured raw data. Once it’s in the data lake, the data can be used for machine learning or artificial intelligence (AI) algorithms and models, or it can be transferred to a data warehouse after processing. 

Data lake examples

Data lakes can be used in a variety of sectors by data professionals to tackle and solve business problems.

• Marketing: In a data lake, marketing professionals can collect data on their target customer demographic preferences from many different sources. Platforms such as Hubspot actually store data in data lakes and then present it to marketers in a shiny interface. Data lakes enable marketers to analyze data, make strategic decisions, and build data-driven campaigns [2].

• Education: This sector has begun using data lakes to track data on grades, attendance, and other performance metrics so that universities and schools can improve their fundraising and policy goals. A data lake provides the right amount of flexibility to handle these data types.

• Transportation: A data lake is used when airline and freight company data scientists cut costs and increase efficiency to support lean supply chain management.

What is a data warehouse?

A data warehouse is a centralized repository and information system used to develop insights and inform decisions with business intelligence. It stores organized data from multiple sources, such as relational databases, and employs online analytical processing (OLAP) to analyze it. The warehouses perform functions such as data extraction, cleaning, transformation, and more.

Data warehouse examples

Data warehouses provide structured systems and technology to support business operations. Some examples include:

• Finance and banking: Financial companies can use data warehouses to provide company-wide access to data. Rather than creating reports using Excel spreadsheets, a data warehouse can generate secure and accurate reports, saving companies time and money.

• Food and beverage: Big companies turn to high-performance enterprise data warehouse systems to run operations and consolidate sales, marketing, inventory, and supply chain data in one place.

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