This repo explores different big data technologies for analyzing songs and user activity data captured by the music streaming app of the ficticious Sparkify startup.
The goal is to examine a number of technologies to a build business intelligence platform from which the data scientists can easily analyze the songs and user activity data collected on the app.
The Sparkify music streaming app collects song metadata and user activity on the app in json log files. The sample of these log files are stored under /data directory.
The song 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.
data/song_data/A/B/C/TRABCEI128F424C983.json
data/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}
The second database 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 are partitioned by year and month. For example, here are filepaths to two files in this dataset.
data/log_data/2018/11/2018-11-12-events.json
data/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.
