Pouring data from different sources for later analysis?

Question

Python

Hello! At work (telecommunications) they gave the task: to analyze the reasons for the outflow of customers in the B2B segment, it is necessary to organize a data mart. And here the data as from internal inf. systems (DB for the most part ORACLE), and from external sources (various sites, services of a tax-legal nature). Immediately I came up with a solution something like this:
- choose PostgreSQL as a database for storage
- write an application in Python that would upload data from internal and external databases - organize the
whole process in queues
information with the history of their accruals and payments, device installations, contracts, contract terminations, etc.
I wanted to know how this could be done most correctly in terms of productivity and support. Or which way to look at all. Since there is not enough experience in this matter, the question actually arose. Suggestions for further data analysis are welcome. Thanks in advance.

Answer 1

To do this, I recommend that you use the power bi tools. For example from Microsoft
This product supports numerous sources of online import, including SQL, as well as their synchronization of data from various sources

Answer 2

The tools used depend on several things:
1. Experience with databases (you or your team)
2. Experience with analytical tools, the ability to see connections in columns of numbers, etc.
3. Clarity of the tasks and a clear understanding of what you need
If experience small or just do not know how to approach the task - use, as Felix correctly noted, MS Power BI. An excellent tool for basic analysis and exploration charts (display data in hundreds of different sections and quickly view, analyzing dynamics, connections, etc.).
If the experience is great and there is a clear understanding of what you want to see, then use Excel with a set of correctly composed queries. At the output, you will get well-formed data arrays. Then do what you want with this array - at least pour it into MS Power BI, at least process it within Excel.