Data Lake vs Data Warehouse is a conversation many companies are having and if they’re not, they should be. However, more often than not, those who are deciding between them don’t fully understand what they are. For this reason, I will be breaking down the details of why one would choose a data lake vs data warehouse in the simplest terms possible. So, let’s get to it and learn the difference, without all the unnecessary technical jargon.
What is a Data Lake?
A data lake is essentially a massive lake of raw, unstructured data. In a data lake, the use case for the data has not yet been determined and the possibilities are endless. Data can be transformed any way the user needs, which makes it especially good for data analysis. Since data in a data lake is unstructured, it can support all file types, including pictures, videos, logs, and more. Typically a data lake is going to be well-suited for data scientists and analysts. They are flexible and highly accessible. Finally, data lakes can provide faster insights into data and can be easily transformed to fit a data scientists’ needs. Keep in mind, data lakes can easily become data swamps, without the right regulations.
What is a Data Warehouse?
On the other hand, a data warehouse is going to have structured data with a well-defined use case. Changing the structure of the data in a data warehouse is time-consuming and can be expensive, so it is best for business professionals who are often seeking specific insights. The data in a data warehouse is constantly being used and is highly relevant to the day-to-day running of a business, whereas the data in a data lake can sit there for years and not be utilized.