udacity-nd027-Data-Modeling-with-Postgres

Sparkify is a music streaming service, created by Udacity to resemble the real-world datasets generated by companies such as Spotify or Pandora. Millions of users play their favorite songs through music streaming services on a daily basis.

Schema for Song Play Analysis

Using the song and log datasets to create a star schema optimized for queries on song play analysis. This includes the following tables.

Fact Table

1. 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

Dimension Tables

2. users - users in the app
   • user_id, first_name, last_name, gender, level
3. songs - songs in music database
   • song_id, title, artist_id, year, duration
4. artists - artists in music database
   • artist_id, name, location, latitude, longitude
5. time - timestamps of records in songplays broken down into specific units
   • start_time, hour, day, week, month, year, weekday

Project Template

The project workspace includes six files:

1. test.ipynb displays the first few rows of each table to let you check your database.
2. create_tables.py drops and creates your tables. You run this file to reset your tables before each time you run your ETL scripts.
3. etl.ipynb reads and processes a single file from song_data and log_data and loads the data into your tables. This notebook contains detailed instructions on the ETL process for each of the tables.
4. etl.py reads and processes files from song_data and log_data and loads them into your tables. You can fill this out based on your work in the ETL notebook.
5. sql_queries.py contains all your sql queries, and is imported into the last three files above.
6. README.md provides discussion on your project.

Project Steps

Create Tables

1. Write CREATE statements in sql_queries.py to create each table.
2. Write DROP statements in sql_queries.py to drop each table if it exists.
3. Run create_tables.py to create your database and tables.
4. 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.

Build ETL Processes

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.

Build ETL Pipeline

• After finished implement etl.ipynb, implement etl.py accordingly, where we process the entire datasets.
• 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.

Results:

artists & time	songs & users