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

Written by Coursera Staff • Updated on

Data lakes and data warehouses are more different than they are similar. Do you know what the key differences are? Find out here.

[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. While a data warehouse is designed to be queried and analysed, a data lake (much like a real lake filled with water) has multiple sources (tributaries or rivers) of structured and unstructured data that flow into one combined site. Data lakes often work best on cloud-based systems, so businesses may need to implement cloud technologies to use this form of data management.

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

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]:

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 modelling) 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

What is a data lake?

A data lake is a storage repository that captures and stores many structured, semi-structured, and unstructured raw data. Once in the data lake, the data can be used for machine learning or artificial intelligence (AI) algorithms and models or transferred to a data warehouse after processing. 

Data lake examples

Data professionals can use data lakes in various sectors 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 store data in data lakes and then present it to marketers in a shiny interface. Data lakes enable marketers to analyse 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 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 centralised repository and information system used to develop insights and inform decisions with business intelligence. Data warehouses store organised data from multiple sources, such as relational databases, and employ online analytical processing (OLAP) to analyse data. The warehouses perform data extraction, cleaning, transformation, and more functions.

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.

Get started with Coursera.

Start your career as a data warehouse engineer today. Enroll in IBM’s Data Warehouse Engineering Professional Certificate on Coursera to learn about SQL statements and queries, design and populate data warehouses, and more. Earn your Professional Certificate in eight months or less.

Article sources


Guru99. “Data Lake vs Data Warehouse: What’s the Difference?,” Accessed July 10, 2024.

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