Project Description

Current project is devoted to query and analyzing collected data on user activity and songs in new music streaming app.

Schema for Song Play Analysis

A star schema optimized for queries on song play analysis was designed and implemented. It 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 Files

The project workspace consists of 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.
5. sql_queries.py contains all your sql queries, and is imported into the last three files above.

Project DataFlow

Below are steps you can follow to get Song Play Database:

Create Tables

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.

Build ETL Processes

You can use the etl.ipynb notebook to debug 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

Script etl.py allows to process the entire data sets. 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.

Query Examples

Some query examples to Song Play Database are presented in test.ipynb notebook.