melvinjosekurian / project-4-data-lake-with-aws-emr

An ETL pipeline that extracts data from S3, processes them using Spark, and loads the data back into S3 as a set of dimensional tables

Data Lake

Introducion

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.
We will 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 Datasets

You'll be working with 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

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. 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

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

And below is an example of what the data in a log file, 2018-11-12-events.json, looks like.

Schema for Song Play Analysis

Using the song and log datasets, you'll need to create a star schema optimized for queries on song play analysis. This includes the following tables.

Fact Table

1.songplays - records in log data associated with song plays i.e. records with page NextSong

• start_time, user_id, level, song_id, artist_id, session_id, location, user_agent

Dimension Tables

2.users - users in the app

• user_id, first_name, last_name, gender, level

3.songs - songs in music database

• song_id, title, artist_id, year, duration

4.artists - artists in music database

• artist_id, name, location, latitude, longitude

5.time - timestamps of records in songplays broken down into specific units

• start_time, hour, day, week, month, year, weekday

Project Template

In addition to the data files, the project workspace includes six files:

1.etl.py reads data from S3, processes that data using Spark, and writes them back to S3.
2.dl.cfg contains your AWS credentials.
3.README.md provides discussion on your process and decisions.

Project Steps

1.Implement the logic in etl.py to load raw data from given S3 buckets to create new tables using spark.
2.Implement the logic in etl.py to load new tables to in parquet format on S3.
3.Create the AWS EMR cluster with spark as processing engine and hdfs as storage in AWS. 4.Copy the etl.py file on hdfs and run it using spark-submit command. Please remove the config related code as it is not required to run this file on EMR, else it will give errors.
5.Check the output S3 bucket for output.
6.Delete your Amazon EMR when finished.

The song play data model is as follows