ETL Practice Project

---

This project uses a couple libraries. These libraries, as well as their uses are:

• requests - Used to get data from API
• pandas - Used to store API data in a DataFrame
• dotenv - Used to keep API and SQL server keys seperate from the main project
• pyodbc - Used to connect to SQL server
• matplotlib - Used for plotting (WIP)

---

---

Extract

The extraction process started with the Tiingo API. I collected historical Apple stock data from the dates 2014-05-20 to 2024-05-21.

Transform

This step is fairly straight forward. Since the data is uniform in its type and doesnt have any null values, I did not transform the data in any way. An expansion of this project could see me take an incomplete data source and transform it further.

Load

For this section, I used a Microsoft SQL Server hosted on my local machine. I used the db_credentials.py file to create the StockData table. From there, I created used the loadDataIntoDb function to load the data held in the DataFrame into the SQL server