A music streaming startup, Sparkify, has grown their user base and song database even more and want to move their data warehouse to a data lake. 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 a data engineer, I have assinged to build 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.
I was able to test the database and ETL pipeline by running queries given to you by the analytics team from Sparkify and compare your results with their expected results.
In this project, I was able to build an ETL pipeline for a data lake hosted on S3. I completed it by loaded the data from S3, and processed it into analytics tables using Spark,then loaded them back into S3. I have used AWS to deploy Spark process.
Ther are two datasets reside in S3:
- Song data: s3://udacity-dend/song_data
- Log data: s3://udacity-dend/log_data
Star schema is created, that include the following tables :
songplaysis a fact table.songs,artists,usersandtimeare dimension tables.
ETL pipeline that extracts the data from S3, processes them using Spark, and loads the data back into S3 as a set of five tables bye execute the etl.py file and configure the dl.cfg by adding the Access Key and secret access key .