Using the song and log datasets, you'll need to create a star schema optimized for queries on song play analysis. This includes the following tables.
- songplays - records in log data associated with song plays i.e. records with page
NextSong
- songplay_id, start_time, user_id, level, song_id, artist_id, session_id, location, user_agent
- users - users in the app
- user_id, first_name, last_name, gender, level
- songs - songs in music database
- song_id, title, artist_id, year, duration
- artists - artists in music database
- artist_id, name, location, latitude, longitude
- time - timestamps of records in songplays broken down into specific units
- start_time, hour, day, week, month, year, weekday
test.ipynb
displays the first few rows of each table to let you check your database.create_tables.py
drops and creates your tables. You run this file to reset your tables before each time you run your ETL scripts.etl.ipynb
reads and processes a single file fromsong_data
andlog_data
and loads the data into your tables. This notebook contains detailed instructions on the ETL process for each of the tables.etl.py
reads and processes files fromsong_data
andlog_data
and loads them into your tables. You can fill this out based on your work in the ETL notebook.sql_queries.py
contains all your sql queries, and is imported into the last three files above.README.md
provides discussion on your project.
Below are steps you can follow to complete the project:
- Write
CREATE
statements insql_queries.py
to create each table. - Write
DROP
statements insql_queries.py
to drop each table if it exists. - Run
create_tables.py
to create your database and tables. - Run
test.ipynb
to confirm the creation of your tables with the correct columns. Make sure to click "Restart kernel" to close the connection to the database after running this notebook.
Follow instructions in the etl.ipynb
notebook to develop ETL processes for each table. At the end of each table section, or at the end of the notebook, run test.ipynb
to confirm that records were successfully inserted into each table. Remember to rerun create_tables.py
to reset your tables before each time you run this notebook.
Use what you've completed in etl.ipynb
to complete etl.py
, where you'll process the entire datasets. Remember to run create_tables.py
before running etl.py
to reset your tables. Run test.ipynb
to confirm your records were successfully inserted into each table.
Do the following steps in your README.md
file.
- Discuss the purpose of this database in the context of the startup, Sparkify, and their analytical goals.
- State and justify your database schema design and ETL pipeline.
- [Optional] Provide example queries and results for song play analysis.
Here's a guide on Markdown Syntax.