The database we all know is simple data storage where all the data of our application is stored. To understand the data lake, we need to understand the data warehouse first.
The data warehouse is the place where the data of the entire enterprise get stored. When you join and combine multiple databases you create a data warehouse. To do analysis using a data warehouse you need to think first about what you want to analyze then load that type of data out of it. When you are talking about business analysis you are mostly talking about data warehouse.
Data warehouse consists of structured data, that you feed into it. But in today's time, unstructured data like log files, etc. are also very important for your business. Previously it was impossible to store and process such data but now it is possible.
The data lake is opposite to the data warehouse, its philosophy is to ''load first, think later''. You can download all the data first in one place and then think about what kind of data analysis you want from that data. Data in the data lake is stored in its raw form. The data lake is like a center reservoir where you can store all kinds of data.
A data warehouse can be used only for a limited purpose related to that enterprise, but data of the data lake can be used for multiple purposes even beyond an enterprise. The data lake is the best data storage for AI and machine learning. It is the best platform for data scientists to play around with the data and come with their own analyses.
Both data warehouse and data lake can work together. You can create multiple data warehouses from a data lake as per your need.
-- The above result is obtained by our Internet Research Algorithm