A music streaming startup, Sparkify, has grown their user base and song database and want to move their processes and data onto the cloud. 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.

s3://udacity-dend/song_data s3://udacity-dend/log_dat

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.

song_data/A/A/B/TRAABJL12903CDCF1A.json

{"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}

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/2018/11/2018-11-01-events.json

{"artist":null,"auth":"Logged In","firstName":"Walter","gender":"M","itemInSession":0,"lastName":"Frye","length":null,"level":"free","location":"San Francisco-Oakland-Hayward, CA","method":"GET","page":"Home","registration":1540919166796.0,"sessionId":38,"song":null,"status":200,"ts":1541105830796,"userAgent":"\"Mozilla\/5.0 (Macintosh; Intel Mac OS X 10_9_4) AppleWebKit\/537.36 (KHTML, like Gecko) Chrome\/36.0.1985.143 Safari\/537.36\"","userId":"39"}

Using the song and event datasets, we'll need to create set of dimensional tables in S3.

Fact Table

1. 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

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, lattitude, longitude
4. time - timestamps of records in songplays broken down into specific units
   • start_time, hour, day, week, month, year, weekday

The main task is 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
|-- local_data                               # ---> Locally downloaded data for testing
    |-- log-data.zip
    |-- song-data.zip
|-- etl.py                                   # ---> Spark script for processing all data local_data dir and storing again data_lake dir
|-- spark_scripts
    |-- cluster_etl.py                       # ---> Main script for processing all data from S3 and storing again to S3
|-- README.md
|-- emr_cluster.py                           # ---> Simple EMRCluster class for starting, terminating, unploading and running spark job
|-- emr_cluster.ipynb                        # ---> Jupyter notebook script for executing EMRCluster class and test data
|-- dl.cfg.dist                              # ---> Template config for AWS credentials
|-- .gitignore

1. Software:

• Python 3.6.x
• pip

2. Python 3rd party libraries:

(ONLINE MODE)

• boto3

pip install boto3

(LOCAL MODE)

• Download Spark
• pyspark

pip install pyspark

1. Clone or download it.
2. Make sure you met all the requirements listed above.
3. Create a file dl.cfg from dl.cfg.dist and fill your AWS credentials. (ONLINE MODE)
4. Open emr_cluster.ipynb and run all cells. (ONLINE MODE)
5. Run emr.py. (LOCAL MODE)