Udacity Data Warehouse(AWS Redshift)

Introduction

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, you are tasked with 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. You'll be able to test your database and ETL pipeline by running queries given to you by the analytics team from Sparkify and compare your results with their expected results.

Project Datasets

• Song data('s3://udacity-dend/song_data'): 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.
• Log data('s3://udacity-dend/log_data'): 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 activity logs from a music streaming app based on specified configurations.

Installation

To run the files in this project first you need to install the following libraries using anoconda.

conda install pandas
conda install psycopg2
conda install boto3
conda install json
conda install configparser
conda install seaborn

You can also install using pip install.

Project Template

1. create_table.py: create fact and dimension tables and staging tables for the star schema in Redshift.
2. etl.py: load data from S3 into staging tables on Redshift and then process that data into analytics tables on Redshift.
3. sql_queries.py: define you SQL statements, which will be imported into the two other files above.
4. testing.ipynb: 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: provide discussion on your process and decisions for this ETL pipeline.
6. dwh.cfg: contains configuration for Redshift database. Please edit accordingly if you plan to use.

Data Schema

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

How to run Project

1. Setup Redshift cluster & IAM role and in AWS and fill in connection details into dwh.cfg
2. Create database structure/schema by running create_table.py.
3. Process and load the data from S3 buckets into above database structure/schema by running etl.py.
4. Delete IAM role and Redshift cluster

Example Results

License and Acknowledgement

I would like to give special thank to Udacity for giving me the change to work on this project. The dataset used in this project can be found at Million Song Dataset.