• AWS S3 - Source datasets.
• AWS Redshift
  >for staging extracted data
  >for storing the resultant data model (facts and dimensions)
  
  

• Fact Table
  songplays: songplay_id, start_time, user_id, level, song_id, artist_id, session_id, location, user_agent
  
  

• Dimension Tables
  users: user_id, first_name, last_name, gender, level
  songs: song_id, title, artist_id, year, duration
  artists: artist_id, name, location, lattitude, longitude
  time: start_time, hour, day, week, month, year, weekday
  
  

There are two json files for

• Song data: s3://udacity-dend/song_data - Data for all songs with their respective artists available in application library.
  
• Log data: s3://udacity-dend/log_data - Data for user events and activity activity on the application.
  
  

• Create sql_queries.py - to design and build tables for proposed data model
  
• Run create_tables.py - to create tables by implementing the database queries from sql_queries.py
  
• Run etl.py - to implement the data pipeline built over the data model which extract, stage and load data from AWS S3 to DWH on AWS Redshift
  
• Design and fire analytical queries on the populated data model to gain insights of user events over streaming application