A music streaming startup, Sparkify, has grown their user base and song database and want to move their processes and data onto the cloud. Their data resides in S3, in a directory of JSON logs on user activity on the app, as well as a directory with JSON metadata on the songs in their app.

As their data engineer, I'm gonna tasked to building an ETL pipeline that extracts their data from S3, stages them in Redshift, and transforms data into a set of dimensional tables for their analytics team to continue finding insights in what songs their users are listening to.

In this project, I'm going to build an ETL pipeline for a database hosted on Redshift. To complete the project, I will need to load data from S3 to staging tables on Redshift and execute SQL statements that create the analytics tables from these staging tables.

1 - STEP 1: Set up AWS on console.
2 - STEP 2: Run create_tables.py to create staging tables and analytics tables (Fact&Dim tables).
3 - STEP 3: Run etl.py to begin ETL process, extract file from S3, load into staging table and tranform data to analytic tables.

I'm going to working with two datasets that reside in S3. Here are the S3 links for each:

'Song data: s3://udacity-dend/song_data'
'Log data: s3://udacity-dend/log_data'

Log data json path: 's3://udacity-dend/log_json_path.json'

The first dataset is a subset of real data from the Million Song Dataset. Each file is in JSON format and contains metadata about a song and the artist of that song. The files are partitioned by the first three letters of each song's track ID. For example, here are filepaths to two files in this dataset.

song_data/A/B/C/TRABCEI128F424C983.json song_data/A/A/B/TRAABJL12903CDCF1A.json

And below is an example of what a single song file, TRAABJL12903CDCF1A.json, looks like.

{"num_songs": 1, "artist_id": "ARJIE2Y1187B994AB7", "artist_latitude": null, "artist_longitude": null, "artist_location": "", "artist_name": "Line Renaud", "song_id": "SOUPIRU12A6D4FA1E1", "title": "Der Kleine Dompfaff", "duration": 152.92036, "year": 0}

The second dataset consists of log files in JSON format generated by this event simulator based on the songs in the dataset above. These simulate app activity logs from an imaginary music streaming app based on configuration settings.

The log files in the dataset you'll be working with are partitioned by year and month. For example, here are filepaths to two files in this dataset.

log_data/2018/11/2018-11-12-events.json log_data/2018/11/2018-11-13-events.json

And below is an example of what the data in a log file, 2018-11-12-events.json, looks like.

Using the song and event datasets, I've created a star schema optimized for queries on song play analysis. This includes the following tables.

Fact Table

- songplays - records in event 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

- 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, lattitude, longitude
- time - timestamps of records in songplays broken down into specific units
    start_time, hour, day, week, month, year, weekday

The project template includes four files:

- create_table.py is where I have facts and dimensions tables for the star schema in Redshift.
- etl.py is where I load data from S3 into staging tables on Redshift and then process that data into your analytics
  tables on Redshift.
- sql_queries.py is where I define the SQL statements, which I had imported into the two other files above.
- README.md is where I explain the role project structure, development process and decisions for this ETL pipeline.