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

Key takeaways

• While a data lake is a storage repository designed to capture and store large volumes of raw data, regardless of type, a data warehouse is a repository for already structured data, designed to be queried and analyzed for very specific purposes.

• Data scientists use a data lake to determine how to reduce costs, and business professionals use a data warehouse to consolidate sales data, market products, and manage inventory.

• Data lake versus data warehouse differences include purpose, schema position, users, accessibility, and the history of these technologies.

An example of a data lake is the education sector, where using data lakes to track grades, attendance, and other performance metrics enables universities and schools to improve their fundraising and policy goals. Discover more about data lakes and data warehouses, including what they are, how they differ from one another, and what they're used for.

If you’ve decided to begin a career in this field, enroll in the IBM Data Warehouse Engineer Professional Certificate. In as little as four months, you can learn about database design, data pipelines, database management, data transformation, Linux commands, and more.

What is a data lake vs. data warehouse?

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.

Read more: What Is a Business Analyst? Career Guide

The key differences between a data lake and a data warehouse are as follows:

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.

• 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 at airline and freight companies cut costs and increase efficiency to support Lean supply chain management.

Do you need a data warehouse if you have a data lake?

To have efficient data processing operations, you should have a data warehouse and a data lake since they perform different things. Data warehouses allow you to store structured data, while data lakes allow you to store any kind of data. You can first land data in a data lake, process, clean, and structure it so it can go into a data warehouse for later analysis.

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.

Discover more with our free resources

Discover fresh insights into your career or learn about trends in your industry by subscribing to our LinkedIn newsletter, Career Chat. Or if you want to keep learning more about working with data, check out these free resources:

• Watch on YouTube: Data Warehouse vs. Database: Which One Do You Need?

• Explore a career: Data Science Career Paths: Explore Roles & Specializations

• Hear from an expert: 6 Questions with an IBM Data Scientist and AI Engineer

With Coursera Plus, you can learn and earn credentials at your own pace from over 350 leading companies and universities. With a monthly or annual subscription, you’ll gain access to over 10,000 programs; just check the course page to confirm your selection is included.

Build job-ready skills with Coursera Plus

Start 7-day free trial

• 
• 
• 
• 

Start 7-day free trial