Udacity-nd027-Data-Warehouse

Project Datasets

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

Song Dataset

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}

Log Dataset

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 will 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

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

1. create_table.py is where you'll create your fact and dimension tables and staging tables for the star schema in Redshift.
2. etl.py is where you'll load data from S3 into staging tables on Redshift and then process that data into your analytics tables on Redshift.
3. sql_queries.py is where you'll define you SQL statements, which will be imported into the two other files above.
4. test.ipynb is where you'll create redshift cluster and create an IAM role that has read access to S3 and verify the result after run etl.py.
5. README.md is where you'll provide discussion on your process and decisions for this ETL pipeline.

Project Steps

Below are steps you can follow to complete each component of this project.

Create Table Schemas

1. Design schemas for your fact and dimension tables
2. Write a SQL CREATE statement for each of these tables in sql_queries.py
3. Complete the logic in create_tables.py to connect to the database and create these tables
4. Write SQL DROP statements to drop tables in the beginning of create_tables.py if the tables already exist. This way, you can run create_tables.py whenever you want to reset your database and test your ETL pipeline.
5. Launch a redshift cluster and create an IAM role that has read access to S3.
6. Add redshift database and IAM role info to dwh.cfg.
7. Test by running create_tables.py and checking the table schemas in your redshift database. You can use Query Editor in the AWS Redshift console for this.

Build ETL Pipeline

1. Implement the logic in etl.py to load data from S3 to staging tables on Redshift.
2. Implement the logic in etl.py to load data from staging tables to analytics tables on Redshift.
3. Test by running etl.py after running create_tables.py and running the analytic queries on your Redshift database to compare your results with the expected results.
4. Delete your redshift cluster when finished.

How to run scripts

Set environment variables KEY and SECRET.

Choose DB/DB_PASSWORD in dhw.cfg.

Create IAM role, Redshift cluster, connect to S3 bucket and configure TCP connectivity

Drop and recreate tables

$ python create_tables.py

Run ETL pipeline

$ python etl.py

Validate the tables

• Run test.ipynb
• Open the Amazon Redshift and use the database info to make a connection.
• Execute query in test.ipynb to check the tables.

Delete IAM role and Redshift cluster

Result: