dataware_housing

Data Warehousing using Redshift and postgres

Project Introduction:

For the 3rd project of the udacity Data Engineering NanoDegree I developed a data warehouse for the music streaming company Sparkify. 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.

Project Description

As their data engineer, I was 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.
The database and ETL pipeline were tested by running queries given to me by the analytics team from Sparkify and compared my results with their expected results.

Project Steps:

Create Table Schemas:

Designed schemas for your fact and dimension tables
Write a SQL CREATE statement for each of these tables in sql_queries.py
Create create_tables.py script to connect to the database and create these tables
Wrote SQL DROP statements to drop tables in the beginning of create_tables.py if the tables already exist. This way, we can run create_tables.py whenever we want to reset the database and test the ETL pipeline.
Launched a redshift cluster and created an IAM role that has read access to S3.
Added redshift database and IAM role info to dwh.cfg.
Tested 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.

Built ETL Pipeline:

Implemented the logic in etl.py to load data from S3 to staging tables on Redshift.
Implemented the logic in etl.py to load data from staging tables to analytics tables on Redshift.
Tested 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.
Deleted redshift cluster when finished.

Project files:

create_table.py is where you'll create your fact and dimension tables for the star schema in Redshift.
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.
sql_queries.py is where you'll define you SQL statements, which will be imported into the two other files above.
dwh.cfg config file, which contains the AWS configuration details to connect to the redshift cluster. This file is not contained in the repository as they are my credentials.

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 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

aabid0193/data_warehousing