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

Written by Coursera Staff • Updated on

Data lakes and data warehouses have several key differences. Can you tell them apart? Read this guide to learn what they are and more.

[Featured image] Three coworkers examine data servers.

Data lakes and data warehouses are storage systems for big data used by data scientists, data engineers, and business analysts. Despite their similarities, though, they're more different than they are similar, and understanding these key differences is important for any aspiring data professional.

In this article, you'll learn all about data lakes and data warehouses, including what they are, how they differ from one another, and what they're used for. At the end, you'll even explore recommended courses to deepen your understanding of these two important data storage systems.

Data lake vs. data warehouse: Key differences

Data lakes, much like real lakes, have multiple sources ("rivers") of structured and unstructured data that flow into one combined site. Data warehouses are designed to be repositories for already structured data to be queried and analyzed for very specific purposes.

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.

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

ParametersData LakeData Warehouse
Data typeRaw (all types, no matter source of structure)Processed (data stored according to metrics and attributes)
Data purposeTo be determinedCurrently being used
ProcessExtract Load Transform (ELT)Extract Transform Load (ETL)
Schema positionAfter data storage, to offer agility and easy data captureBefore data storage, to offer security and high performance
UsersData scientists, those who need in-depth analysis and tools (such as predictive modeling) to understand itBusiness professionals, those who need it for operations
AccessibilityAccessible and easy to updateComplicated to make changes
HistoryRelatively new for big dataThe concept has been around for decades

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 all types of raw data. The data can be structured, semi-structured, and unstructured. Once it’s in the data lake, the data can be used in machine learning or artificial intelligence (AI) algorithms and models for business purposes. It can also 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. Some examples include:

  • Marketing: Marketing professionals can collect data on their target customer demographic’s preferences from many different sources in a data lake. Platforms such as Hubspot 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: The education 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 types of data.

  • Transportation: A data lake is used when data scientists of airline and freight companies 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. Like an actual warehouse, data gets processed and organized into categories to be placed on its "shelves" that are called data marts.

Data warehouses store organized data from multiple sources, such as relational databases, and employ online analytical processing (OLAP) to analyze data. The warehouses perform functions on the data such as 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 the data. Rather than using Excel spreadsheets to create reports, a data warehouse can generate reports that are secure and accurate, saving companies time and money.

  • Food and beverage: Big conglomerates (think Nestlé and PepsiCo) turn to high-performance enterprise data warehouse systems that enable them to run operations, consolidating sales, marketing, inventory, and supply chain data all in one place.

Get started with Coursera

Start your career as a data warehouse engineer today.

In IBM’s Data Warehouse Engineering professional certificate, you'll learn all about SQL statements and queries, how to design and populate data warehouses, and more. Earn your professional certificate in three months or less.

Amazon's Introduction to Designing Data Lakes on AWS course, meanwhile, will help you understand how to create and operate a data lake in a secure and scalable way, without previous knowledge of data science.

Article sources


Guru99. “Data Lake vs Data Warehouse: What’s the Difference?,” Accessed December 20, 2023.

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