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 into what songs their users are listening to.

There are two datasets that reside in S3:

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 file paths to two files in this 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 activity logs from a music streaming app based on specified configurations.

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.

Staging Tables:

Fact Table:

Dimension Tables

The ETL Pipeline consists of 2 steps:

First, I will extract data from S3 and staging them in Redshift using COPY Command. The staging tables are: staging_event & staging_song. Transform data into a set of dimensional tables and fact table with Star Schema. Then, load data from staging tables to analytics tables on Redshift with INSERT INTO statment. To avoid duplication data, use SELECT DISTINCT.

To create tables

To run ETL pipeline

Connect Power BI to Redshift to create dashboard: