Chad Dubiel, David Martinez, Katy Fuentes

Correlation between cryptocurrency pricing and Covid case counts.

https://github.com/cdubiel08/ETL-Project-Group-9

• API Key - SFOX https://www.sfox.com/developers/?python#market-data or Gemini https://docs.gemini.com/websocket-api/#market-data

• Covid - https://www.kaggle.com/imdevskp/corona-virus-report
• Cryptocurrency Historical Chart - https://www.kaggle.com/mczielinski/bitcoin-historical-data

• What useful investigation could be done with the final database? Use the output and compare to markets, commodities, or US dollar.
• Whether final database will be relational or non-relational. Why? Relational because the information will be interconnected based on a timeframe.

Dates not a good join method, need a unique ID for primary key

• Pandas - for data formatting, date cleaning, reduce columns
• Mongo - better for skipping null values which would skip data column, any covid/crypto overlaps captured  

• At least 2 (or more) sources
• If possible, try to incorporate a web API as one of your data sources.

• Within Jupyter, build out the ETL process to extract your data from their sources, apply some level of transformation, and load the resulting data to a database (relational or non-relational)

• Build a Flask application that has a route that will execute a query to your database and return the results in JSON format.

• Write up a short report that details your 3 ETL steps.
• More details on a later slide.

• Store all of your project files in a well-organized project repository
• Each member of your team will submit a link to your project repo to BCS by the end of class Tuesday

• What data sources you chose and why?
• Detailing the process of the extraction, transformation, and loading steps
• Explain why you have performed the types of transformation you did
• Why you chose the type of final database
• Schema of the tables/collections in the final database
• Hypothetical use case(s) for your database