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项目作者： robertgv

项目描述 ：
Udacity Data Engineering Nanodegree Program
高级语言： Jupyter Notebook
项目地址: git://github.com/robertgv/Data_Modeling_with_Postgres.git
创建时间： 2020-05-04T16:25:15Z
项目社区：https://github.com/robertgv/Data_Modeling_with_Postgres
开源协议：MIT License
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Data Modeling with Postgres

This project is the first project of the Data Engineering Nanodegree Program of Udacity.

In this project, I will apply what I have learned on data modeling with Postgres and I’m going to build an ETL pipeline using Python and Postgres.

Introduction

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.

Data

In this ETL pipeline there are two types of data:

  • 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.

  • 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.

Database schema

The database schema includes the following tables:

Fact Table

  • 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

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, latitude, longitude
  • time: Timestamps of records in songplays broken down into specific units
    • start_time, hour, day, week, month, year, weekday

Project structure

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

  1. test.ipynb displays the first few rows of each table to let you check your database.
  2. create_tables.py drops and creates your tables. You run this file to reset your tables before each time you run your ETL scripts.
  3. etl.ipynb reads and processes a single file from song_data and log_data and loads the data into your tables. This notebook contains detailed instructions on the ETL process for each of the tables.
  4. etl.py reads and processes files from song_data and log_data and loads them into your tables. You can fill this out based on your work in the ETL notebook.
  5. sql_queries.py contains all your sql queries, and is imported into the last three files above.
  6. README.md provides discussion on your project.

Installation

To run the files in this project first you need to install the following libraries.

Use the package manager pip to install the following packages.

  1. pip install pandas
  2. pip install psycopg2

Another option is to install Anaconda and use conda to install this packages.

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

License

This project is under the license MIT.