Data Lake vs Data Warehouse

When you’re dealing with significant amounts of data, it’s crucial that you choose the right form of storage. Read on for information about and a comparison between 2 potential solutions: data lakes and data warehouses.

Why Are They Used?

Although data lakes and data warehouses are often grouped together, the only thing they really have in common is that they both contain large amounts of data. However, the structure of data and its purpose differs, as do the processing methods used and the area they are used at.

To optimize usage of each, it’s important to understand how they differ.

Key Differences Between A Data Lake & Data Warehouse

A data lake stores raw data. This data is not yet destined for any specific purpose. It’s easily accessible, meaning it can be updated quickly. Data scientists are its end users.

In contrast, a data warehouse stores processed data. This is data that is already being used for a specific purpose. The process of making changes to this data can be more complex, and the end users of a data warehouse are the decision makers and entrepreneurs making use of the data.

Raw or Processed Data?

If you’re working with raw data, then you’ll use a data lake. Raw data is known by many other names such as source data, primary data, or even atomic data. All of these terms refer to data that has not yet been processed. In this form, it is useless.

Data processing is what transforms data into something that can be used and actioned. When data is processed, information is the result. This information can be helpful in guiding business decisions.

To turn raw data into information, certain steps much be taken including selective extraction, organization and analysis, and formatting for easy understanding. Data warehouses contain this processed data. However, processed data from one system could be used as raw data in another! This would require another data lake.

Data Scientists or Business Professionals?

Unless you’re a data scientist, you’re likely to find yourself very confused by a data lake. Unprocessed data has not been transformed into easily consumable information yet. When you look at this raw, unstructured data, it may be difficult to make any sense of it. That’s why, if you’re a business professional, you aren’t the typical end user of a data lake.

Data scientists take the raw data from the data lake and process it. The tools and processes that they apply are able to translate the data into useful information.

This makes it easier for every person in a business to take action based on the knowledge gained.

This is one major difference between data lakes and data warehouses: who actually uses them! Whether you’re a data scientist or a business professional will determine which is most relevant to your purposes.

Flexible Or Secure?

Of course, the data itself is more difficult to understand in a data lake as opposed to a data warehouse. However, this isn’t what is meant when accessibility and ease of use is discussed in this context. Data lakes have no architectural structure, so they’re easy to access and easy to change – even if they’re difficult to understand!

If you need to make regular changes to data, a data lake is preferable to a data warehouse. Data warehouses have a more structured design – they are harder to access and adapt. Making changes can be costly and involve a time investment.

Although the data in a data warehouse is already processed and so much more straightforward to comprehend and use, the data warehouse itself has structural limitations that make it less accessible than a data lake. With a data lake, you can jump right in!

Which Is Right For Me? A Data Warehouse or Data Lake?

Bear in mind that you may need both! Data lakes will allow you to harness big data, but data warehouses will store processed data whose information can be used for business. Here are how some industries use these storage systems.


Data warehouses have often been a source of problems in the healthcare industry.  Unstructured data such as physician’s notes can make it difficult to obtain the necessary real-time insights. Therefore, data warehouses are less than ideal. In a data lake, you can combine structured and unstructured data, which is a more practical solution.


In an education environment, data about attendance and student grades can be used to predict and prevent problems. Because of the many students and their many subjects, much of this vast data is raw. Educational institutions have benefited from the use of data lakes and their relative flexibility.


What makes a data warehouse a better storage model in a financial context? The fact that it can be accessed by the entire company. It’s insights aren’t restricted to a data scientist.

Data warehouses may not be the most cost-effective storage solution; however, in general, they represent the most effective solution for a financial company’s needs.


What’s great about a data lake is that its insights allow people to make predictions. This can be very useful in the transportation industry. For example, data could be examined to determine cost-cutting measures that can be implemented.


Because a data warehouse is built to support data from across all departments of an organisation, this could be used, for example, to generate insightful information from the sales team that could guide future marketing efforts.

Need Help Choosing?

It’s crucial that you choose the right data storage solution for your needs.

Still unsure whether a data lake or a data warehouse would best suit your company? No problem! Simply contact the experts at Gravity Data. We’ll be happy to talk you through your options.