How to analyse multiple csv files very efficiently?
For me I usually merge the file into a DataFrame and save it as a pickle but if you merge it the file will pretty big and used up a lot of ram when you used it but it is the fastest way if your machine have a lot of ram.
Storing the database is better in the long term but you will waste your time uploading the csv to the database and then waste even more of your time retrieving it from my experience you use the database if you want to query specific things from the table such as you want a log from date A to date B however if you use pandas to query all of that than this method is not very good.
Sometime for me depending on your use case you might not even need to merge it use the filename as a way to query and get the right log to process (using the filesystem) then merge the log files you are concern with your analysis only and don't save it you can save that as pickle for further processing in the future.
I have nearly 60-70 timing log files(all are .csv files, with a total size of nearly 100MB). I need to analyse these files at a single go. Till now, I've tried the following methods :

Merged all these files into a single file and stored it in a DataFrame (Pandas Python) and analysed them.
Stored all the csv files in a database table and analysed them.

My doubt is, which of these two methods is better? Or is there any other way to process and analyse these files?
Thanks.