shrikantnaidu/Data-Lake-with-Spark

Data Lake with Spark

Data-Lake-with-Spark

Context

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.

The objective is 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.

Database schema

Table Overview

Table	Description
songplays	Table for the songs played
users	Table for the user data
songs	Table for the songs data
artists	Table for the artists data
time	Table for time-related data

Fact Table

• songplays - records in event 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

Dimension 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

Project Structure

Files used on the project:

1. etl.py - reads and processes files from song_data and log_data and loads them into your tables.
2. dl.cfg - contains the credentials to use cloud resources.
3. README.md - documentation of the process, provides execution information on the project.

Process Overview

1. Read the data from the S3 bucket.

   • Song data: s3://udacity-dend/song_data

   • Log data: s3://udacity-dend/log_data

2. Process data using spark.

   The script reads song_data and load_data from S3 and transforms the data in the S3 bucket to create five different tables listed below :

   Fact Table:

   • songplays : records in log data associated with song plays i.e. records with page NextSong

   Dimension Tables:

   • users : users in the app.

   • songs : songs in music database.

   • artists : artists in music database.

   • time : timestamps of records in songplays broken down into specific units Fields.

3. Load the data back to the S3 bucket.

   Writes the data in parquet format to different table directories in the S3 bucket.