Data-Lake

Project Description

Applied the knowledge of Spark and Data Lakes to build an ETL pipeline for a Data Lake hosted on Amazon S3

we have to build an ETL Pipeline that extracts their data from S3 and process them using Spark and then load back into S3 in a set of Fact and Dimension Tables. This will allow analytics team to continue finding insights in what songs their users are listening. Will have to deploy this Spark process on a Cluster using AWS

How to create and run EMR cluster

https://towardsdatascience.com/how-to-create-and-run-an-emr-cluster-using-aws-cli-3a78977dc7f0

Project Datasets

Song Data Path --> s3://udacity-dend/song_data 

Log Data Path --> s3://udacity-dend/log_data 

Log Data JSON Path --> s3://udacity-dend/log_json_path.json

Song Dataset

The first dataset is a subset of real data from the Million Song Dataset(https://labrosa.ee.columbia.edu/millionsong/). 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:

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. The log files in the dataset with are partitioned by year and month. For example:

log_data/2018/11/2018-11-12-events.json log_data/2018/11/2018-11-13-events.json

And below is an example of what a single log file, 2018-11-13-events.json, looks like.

{"artist":"Pavement", "auth":"Logged In", "firstName":"Sylvie", "gender", "F", "itemInSession":0, "lastName":"Cruz", "length":99.16036, "level":"free", "location":"Klamath Falls, OR", "method":"PUT", "page":"NextSong", "registration":"1.541078e+12", "sessionId":345, "song":"Mercy:The Laundromat", "status":200, "ts":1541990258796, "userAgent":"Mozilla/5.0(Macintosh; Intel Mac OS X 10_9_4...)", "userId":10}

Schema for Song Play Analysis

A Star Schema would be required for optimized queries on song play queries

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 Template

Project Template include three files:

1. etl.py reads data from S3, processes that data using Spark and writes them back to S3

2. dl.cfg contains AWS Credentials

3. README.md provides discussion on your process and decisions

ETL Pipeline

Load the credentials from dl.cfg Load the Data which are in JSON Files(Song Data and Log Data) After loading the JSON Files from S3 4.Use Spark process this JSON files and then generate a set of Fact and Dimension Tables Load back these dimensional process to S3 Final Instructions

Write correct keys in dl.cfg Open Terminal write the command "python etl.py" Should take about 2-4 mins in total