I have an Excel of raw panel data (cross sectional + time series) sent to me by customers (I cannot change this). For those not familiar, imagine the gdp time series for each country. I need to transfer this data into a database and I am about to choose the best technology, either a SQL (Microsoft SQL Server), or No SQL (MongoDB).
In principle I am quite sure you can use both, but probably an SQL-like is less suitable. My comments:
SQL: I would need to replicate the name of the country as many times as the timestamp avalilable for its gdp, and stacking each country one below the other. When I need to update (because every 3-months a new gdp value comes out), I would need to code complex queries in order to correctly insert the new value where a country ends but before the next one starts (FYI: to insert data into the db I will have a python code which push new data into the db);
NoSQL: here I can have a collection named GDP, and documents related to each country, so for the document "UK", I'd have a key called (time-series) and a value like [[1-1-2022, 2.3], [1-4-2022, 3.1], ..] (list of list where the first is the timestampt and the second value the gdp) and so on;
in addition since this data are quite messy it can happen that from time to time some data is missing so a NoSQL simply will not have a value, which is less problematic than a SQL. To me, NoSQL is better in this case.
Since in this forum there are expert people (I am not..), I would like to know if my reasoning is right, and for panel data a NoSQL (es. MongoDB, because is free..) type is more suitable.