A startup called Sparkify wants to analyze the data they've been collecting on songs and user activity on their new music streaming app. The analytics team is particularly interested in understanding what songs users are listening to. Currently, they don't have an easy way to query their data, which resides 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.
They'd like a data engineer to create a Postgres database with tables designed to optimize queries on song play analysis, and bring you on the project. Your role is to create a database schema and ETL pipeline for this analysis. You'll be able to test your database and ETL pipeline by running queries given to you by the analytics team from Sparkify and compare your results with their expected results.
In this project, you'll apply what you've learned on data modeling with Postgres and build an ETL pipeline using Python. To complete the project, you will need to define fact and dimension tables for a star schema for a particular analytic focus, and write an ETL pipeline that transfers data from files in two local directories into these tables in Postgres using Python and SQL.
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
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.
run python create_tables.py to bulid to create table run python etl.py to use data pipline and to insert data from (song/log) files into the table
create_tables.py: Clean previous schema and creates tables. sql_queries.py: All queries used in the ETL pipeline. etl.py: Read JSON logs and JSON metadata and load the data into generated tables.
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.
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
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
in ETL we process the log and song data that are in json format song data have song and artist info that will use it in songs and artist tables. in log data we can get the data for time table and users table