Data lake and data warehouse are widely used for big data storage, but, although both are data storehouses, these are not interchangeable terms. A data lake is a large set of raw data, which does not yet have a defined purpose. Instead, a data warehouse is a repository of data that is already structured and filtered and has been processed for a specific purpose.
These two types of data storage are often confused, but they are much more different than it may seem. In fact, the only thing they have in common is that they contain large amounts of data.
It is important to make the distinction, as data lake and data warehosue serve different purposes and they require a different approach to be properly optimized.
The differences between data lake and data warehouse
Some of these main differences are the structure of data, the processing methods, the area they are used at, and what the purpose of the data is.
Thus, a data lake stores raw data that does not yet have a specific purpose. Its end users are data scientists and it has high accessibility. Moreover, in a data lake, because of its easy accessibility, the data can be updated quickly.
On the other hand, a data warehouse has processed data that is already in use and therefore has a specific purpose. The end users of a DW are usually entrepreneurs and business people and it is somewhat more complicated to make changes in the data it contains.
Benefits of each type of storage
The main difference between the concepts is surely the variable structure of the raw data versus the processed data. Since data lakes usually store raw data, their storage capacity must be higher than that of data warehouses. Having this raw data has many benefits, such as being able to analyze it quickly and for any purpose. However, without proper data quality and data governance measures, data lakes can become an untreatable data container from which little value can be extracted.
The benefits of a data warehouse are also interesting: since they only store processed data, they save a lot of storage space, which translates into money savings. Furthermore, because the data is processed, it is much more comprehensible and accessible to a less technical public.
Beyond their storage purpose, these two concepts are quite different. Data lakes, because of their unstructured content, can be complex to navigate and require a data scientist, while data warehouses are more suitable for an enterprise and to be handled by less technical users. Due to all these differences, each company must evaluate with experts which of the types is more convenient for them in relation to what they are going to use them for.