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

Data lake or data warehouse - what do they do and which one is right for you?

Dave Kellermanns
Dave Kellermanns, July 8, 2016 10:15 am
Blog > Data Warehousing | Big Data | AWA | Workload Automation > Data Lake vs Data Warehouse: What's the Difference?

Data warehouses and data lakes are two different types of data storage repository, but what are the differences between the pair? The data warehouse integrates data from different sources and suits business reporting. The data lake stores raw structured and unstructured data in whatever form the data source provides. It does not require prior knowledge of the analyses you think you want to perform. 

What is a Data Lake?

A data lake is a storage repository that holds a vast amount of raw data in its native format until it is needed. While a hierarchical data warehouse stores data in files or folders, a data lake uses a flat architecture to store data.

What is a Data Warehouse?

A core component of business intelligence, the data warehouse is a central repository of integrated data from one or more disparate sources, and it’s used for reporting and data analysis. When the board makes a strategic decision on its future, or a call center agent reviews a customer’s profile—the data is typically being sourced from a data warehouse.

Which Should You Choose?​

For an analogous definition of both structures, who better to turn to than the person credited with coining the phrase in the first place: James Dixon, the founder of Pentaho, the Big Data analytics company. He explains, “Think of a data warehouse as a store of bottled water—it’s cleansed, packaged, and structured for easy consumption. The data lake meanwhile is a large body of water in a more natural state. The contents of the data lake stream in from a source to fill the lake, and various users of the lake can come to examine, dive in, or take samples.”

We can classify the key differences like so:

1. Data Retention

Put simply, data lakes retain all data, while data warehouses do not. During the data warehouse development phase, decision are made about which data sources to use, and which business processes are required. If data isn’t required to answer specific questions or in a defined report, it is often excluded from the warehouse in order to reduce cost and optimize performance. Meanwhile, a data lake stores all the data—relevant or not. This possible because the lake resides on lower-cost storage hardware

2. Data Type

Most data warehouses store transaction system data, or quantitative metrics; ignoring unstructured sources such as images, text, or sensor data. Why? Because it’s expensive to store them. Data lakes aren’t so picky. They absorb all data—irrespective of volume and variety. It is stored in its raw form and only transformed when it is needed. It’s called “Schema on Read” vs. the “Schema on Write”.

3. User

The data lake users are more cosmopolitan than those that use the data warehouse. It supports people with “Operations” in their title, who are using the data to access reporting data quickly and get analytics information to the board for accelerated decision-making. It supports the users performing more in-depth data analysis, perhaps using a data warehouse as a source and then accessing the source systems for more analysis. And the data lake supports users wanting even deeper-dive analysis. Data scientists, for example, mashing together different types of data and come up with entirely new questions to be answered.

4. Changes

Business today is all about agility; however, many data warehouses are not configured for rapid change. The complexity of the data loading process and the work done to make analysis and reporting easy, make change unnecessarily slow and expensive. Not so in the data lake. Because data is stored in its raw format and is always accessible, users can go beyond the structure of the warehouse to explore data in novel ways and answer their questions at their pace.

This is a valuable synopsis of the differences between both environments:


Data warehouse


Data lake

Structured, processed


Structured / semi-structured / structured / raw




Expensive for large data volumes


Designed for low-cost storage

Less agile; fixed configuration


Highly agile; configure as required




Business professionals


Data scientists et al



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Dave Kellermanns

Dave Kellermanns

Dave Kellermans is Chief Automation Architect at Automic. In this role he works with a variety of Fortune 100 companies to review and strategize around their automation strategy and start innovating for the benefit of the business. He truly believes that “keeping the lights on” should not be the primary function of IT.