We are implementing Data lake,
Data Warehouse

Data Warehouse is a complex of structured data from the entire enterprise.
‍It consists of warehouses for various objects (orders, prices, goods, etc.). The data structure is determined in advance. After all, businesses know what systems are and how they are used for the purposes of this.

Repositories independent of each other and can run on different servers in different locations. So the refusal of any of them will not cause problems with the rest.

Data Warehouse allows it is easy to connect any analytics system.
It is easy to filter data by open fields and search for the objects you need.

Data Warehouse improves data exchange between IT systems:
1. It reduces the load on the systems;
2. It weakens the coherence of system changes;
3. It allows you to aggregate and transform data however you like.

Learn more about integrations here.

What is Data Lake? Let's look at a simple example of an order management system (OMS).

For example, the OMS system keeps a history of data on the status of all orders. This data is important for analytics, but it only clogs the system itself. This amount of historical data nicer put it into shared storage, so that do not overload the system.

It is impossible to take into account all the formats for using this data in the future. Today we need a report on the amount of time it takes for an order to move from one status to another. After a month, you will need to use order data in a different way and in a different format.

To Data Lake is created to store data for future use - enterprise data lake. Data enters such a repository as it is. When it becomes clear what to use this data for and how, analysts will be able to start processing it.