Loads S3 json files into the spark engine, transform them, and output them as partioned parquet analytics file in S3.

Experimentation and learning of S3, Spark and Data Lakes. This project was created as part of the Data Engineering Nano Degree, run by Udacity.

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, processes them using Spark, and loads the data back into S3 as a set of dimensional tables. This will allow their analytics team to continue finding insights in what songs their users are listening to.

The pipeline processes 2 types of JSON data file

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

The log datasets contain activity logs from the music streaming app, and are partitioned by year and month.

Spark Amazon S3 Parquet

Built with

• The PySpark API for Spark (Python)
• Python, SQL and PySpark for ETL pipeline

• Utilizes Spark for scalable processing
• Simulates a common data lakes analytical flow
• Outputs to parquet files to allow for fast analytics processing

The process is typically run in the following order:

• etl.py - to load, process and output data

etl.py

• loads files from input storage
• transforms data using Spark
• outputs to an analytics folder in S3