Data Lake vs Data Warehouse

Data lake vs data warehouse: which do I choose?

The short answer to this question is, large organizations often need both. Data lakes were born out of the need to harness big data and benefit from the raw, granular structured and unstructured data which is needed primarily by data scientists. Data warehouses, however, store processed and structured data which is more suitable for the business masses, who can understand the information presented in a more summarised and visually interpretable format.

There are several differences between a data lake and a data warehouse: data structure, ideal users, processing methods, and the overall purpose of the data are the key differentiators. 

 Data LakeData Warehouse
Data StructureRawProcessed
Purpose of DataNot Yet DeterminedCurrently In Use
UsersData ScientistsBusiness Professionals
AccessibilityHighly accessible and quick to updateMore complicated and costly to make changes

As cloud computing is now mainstream and scalable, businesses are having to handle exponential growth in data volumes, and there’s a greater drive of time to value in decision making. The critical decision is not necessarily either or, but more about which is most fit for purpose, both in a business application context and given a company’s business maturity in end-user self-service analysis. This is supplemented by the nature of the analysis which is being undertaken, in both known trends in dataset or the desire to establish new patterns and insights through more diverse, granular, real-time and varied data sets.

Here are some explanations of how different sectors may work and use data and thus whether a data lake or a data warehouse is better suited to that sector.

Healthcare: data lakes store unstructured information

Data warehouses have been used for many years in the healthcare industry, but it has never been hugely successful. Due to the unstructured nature of much of the data in healthcare (physicians notes, clinical data, etc.) and the need for real-time insights, data warehouses are generally not an ideal model.

Data lakes allow for a combination of structured and unstructured data, which tends to be a better fit for healthcare companies.

Education: data lakes offer flexible solutions

Recently, the value of big data in education has become enormously apparent, particularly throughout the pandemic. Data about student grades, attendance, this is not only a great help in failing students to get back on track, but can actually help predict potential issues before they occur. Flexible big data solutions have also helped educational institutions streamline billing, improve fundraising, and more.

Much of this data is vast and very raw, so many times, institutions in the education sphere benefit best from the flexibility of data lakes.

Finance: data warehouses appeal to the masses

A data warehouse is often the best storage model because it can be structured for access by the entire company rather than a specific data scientist.

Big data has helped the financial services industry make big strides, and data warehouses have been imperative in making those strides. Financial services companies may be swayed away from such a model because it is more cost-effective, but not as effective for other purposes.

Transportation: data lakes help make predictions

Much of the benefit of data lake insight lies in the ability to make predictions.

In the transportation industry, particularly in supply chain management, the prediction capability that comes from flexible data in a data lake can have huge benefits, namely cost cutting benefits realized by examining data from forms within the transport pipeline.

The importance of choosing a data lake or data warehouse

The “data lake vs data warehouse” conversation has likely just begun, but the key differences in structure, process, users, and overall agility make each model unique. Depending on your company’s needs, developing the right data lake or data warehouse will be instrumental in growth.