Difference between Data Lake and Data Warehouse

Big Data

Difference-between-Data-Lake-and-Data-Warehouse1-300x189 Difference between Data Lake and Data WarehouseData repositories and data warehouses have been around for decades by now. Now with the recent advancement of Data Lake, there arises a natural comparison between the duos. There exist many fundamental differences that separate data warehouses from data lakes, starting from the kind of data, its storage to how it is processed.
Data Lake does not require special hardware or software but data warehouse does, this is a key difference between the two. Secondly, Data lakes are more flexible as they hold huge amounts of raw, unstructured data in its native format, whereas data warehouse consists of structured data collected in folders, rows, and columns. The other difference includes schema-on-read and schema-on-write. Data warehouse uses schema-on-write, which is a logical description of the entire database, leading to a thorough knowledge of the data structure before saving. data lakes use schema-on-read, in which you can format it while reading and processing including log files, web files, data which have no structure that can be figured out later.
Data lakes don’t have an underlying database but use a flat file system. Data warehouse consists of a database which consists of data and columns. This might take some time in inserting the data in the database, but during a query, it will be processed faster than in a data lake.