dokisri/data-lakes-with-spark

Udacity Project for Data Lakes with Spark

Data Lakes with Spark

Introduction

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 their data engineer, we 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.

Project Datasets

The below two datasets that reside in S3. Here are the S3 links for each:

Song data: s3://udacity-dend/song_data Log data: s3://udacity-dend/log_data

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

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

Using the above datasets we have created a star schema and having following tables:

Fact Table

**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

Dimensional Tables

**users** - users in the app
user_id, first_name, last_name, gender, level
**songs** - songs in music database
song_id, title, artist_id, year, duration
**artists** - artists in music database
artist_id, name, location, lattitude, longitude
**time** - timestamps of records in songplays broken down into specific units
start_time, hour, day, week, month, year, weekday

Instructions

Added AWS configuration details into dl.cfg

Running etl.py through terminal use "python etl.py" command to execute

etl.py reads the data from S3 buckets and process the data using Spark with temporary storage and then writes down the data back into S3 bucket.

Delete AWS configuration details from dl.cfg