essraahmed/Data-Lake-with-Spark

Data Lake with Spark

Data Lake 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. 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.

Project Dataset

There are two datasets that reside in S3:

• 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 file paths 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 activity logs from a music streaming app based on specified configurations.

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

Database Schema Design

Fact Table:

1. 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

Dimension Tables

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

Project Template

Project files

1. dl.cfg: Contains AWS credentials.
2. etl.py: Reads data from S3, processes that data using Spark, and writes them back to S3.
3. README.md: Provides discussion on the project.

ETL pipeline

• Extract data from AWS S3, Song data and Log data.
• Transform to create dimenstional and fact tables using Apache Spark.
• Load them back to AWS S3 Data Lake partitioned parquet files.
  We used Parquet format because: Low storage consumption and higher execution speed.

Confguration

To get AWS Credentials:

1. Create IAM User with AmazonS3FullAccess Policy.
2. Then you will get the AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY.

How to run the Python Scripts

Run etl.py.

python etl.py

Author

Esraa Ahmed |

Created on 10/09/2022