SQLite3 / SSMS

SQLite3

I have always enjoyed barebones approaches to programming, and beginning to learn SQL meant downloading and playing with SQLite3 on my personal computer

This is a dataset on movies and their ratings. I do an exploratory analysis to see what might be a good movie to watch! First I check that the table has been loaded properly by pulling up Id, vote averages and the number of votes.

Lets look at the movies with the highest average vote.

I'm not sure about you, but I don't trust the top movies having only one person voting, lets expand the search.

By only pulling up movies that are above 700 votes, were are left with much more reasonable metrics in our vote count. By connecting Titles to the query we see that some of the top movies here are Fight Club, Once Upon a Time in America, and The Shawshank Redemption, all great choices!

SQLServer Management System

I really enjoy the barebones approach to programming, but sometimes tools just make things so much easier. Using SSMS to convert CSV's to SQL files, and drawing from those files directly into visualization tools like PowerBi just saves so much time!

Flat File Imports

SSMS can import CSV's in just a few clicks using the import flat file feature

This process allows for some transformation of data types as well, which is also an option when pulling the data into Power BI.

Now lets connect SSMS to Power BI

The first step is to create an api to create a connection between the SQL server and Power BI, I did this using Flask in Python.

And by using PowerBI's from web feature,

We have our data directly from SSMS through the API

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