Bottom Line: Data Lake vs. Data Warehouse
While both data lakes and data warehouses are repositories for storing large amounts of data, their differences make them better suited to different use cases. It comes down to what users want out of the data.
If they know what they are looking for—monthly sales reports or in-store vs. website traffic, for example—then a data warehouse is a better choice. Organizations that want more flexibility to search for more amorphous information—what time of day is web traffic busiest, or how do weather patterns impact sales—then a data lake is a better fit.
Healthcare organizations, educational institutions, and businesses in the transportation industry could benefit from the flexibility to store both structured and unstructured data—all three industries generate massive amounts of raw data used for a wide range of purposes.
But if the goal is strictly business analysis, a data warehouse is a better choice. Data warehouses designed to process structured data and provide insights and reports that can give organizations a better understanding of their customer base, pricing models, historical sales data, market trends over time, and more.
Enterprises in the financial or business sectors that use vast volumes of structured data can make it available across the organization rather than limiting it to use by a handful of data scientists, making it much more useful for their needs.
For many enterprises, the choice should not be “data lake or data warehouse,” because the two are complementary. The best approach for some cases is to implement both and use them in tandem. Organizations that are already using a data warehouse might implement a data lake to store new data sources, for example, providing a repository for archival data moved out of the warehouse.
Read next: What is a Data Lakehouse?