Data Lake vs Data Warehouse: Key Differences

Data lakes and data warehouses are both widely used for storing big data, but they are not interchangeable terms. A data lake is a vast pool of raw data, the purpose for which is not yet defined. A data warehouse is a repository for structured, filtered data that has already been processed for a specific purpose.

The two types of data storage are often confused, but are much more different than they are alike. In fact, the only real similarity between them is their high-level purpose of storing data.

The distinction is important because they serve different purposes and require different sets of eyes to be properly optimized. While a data lake works for one company, a data warehouse will be a better fit for another.

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.

Raw data is data that has not yet been processed for a purpose. Perhaps the greatest difference between data lakes and data warehouses is the varying structure of raw vs. processed data. Data lakes primarily store raw, unprocessed data, while data warehouses store processed and refined data.

Because of this, data lakes typically require much larger storage capacity than data warehouses. Additionally, raw, unprocessed data is malleable, can be quickly analyzed for any purpose, and is ideal for machine learning. The risk of all that raw data, however, is that data lakes sometimes become data swamps without appropriate data quality and data governance measures in place.

Data warehouses, by storing only processed data, save on pricey storage space by not maintaining data that may never be used. Additionally, processed data can be easily understood by a larger audience.

The purpose of individual data pieces in a data lake is not fixed. Raw data flows into a data lake, sometimes with a specific future use in mind and sometimes just to have on hand. This means that data lakes have less organization and less filtration of data than their counterpart.

Processed data is raw data that has been put to a specific use. Since data warehouses only house processed data, all of the data in a data warehouse has been used for a specific purpose within the organization. This means that storage space is not wasted on data that may never be used.

Data lakes are often difficult to navigate by those unfamiliar with unprocessed data. Raw, unstructured data usually requires a data scientist and specialized tools to understand and translate it for any specific business use.

Alternatively, there is growing momentum behind data preparation tools that create self-service access to the information stored in data lakes.

Learn more at, “What is Data Preparation?” →

Processed data is used in charts, spreadsheets, tables, and more, so that most, if not all, of the employees at a company can read it. Processed data, like that stored in data warehouses, only requires that the user be familiar with the topic represented.