Project: Data Modeling with 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.

Project Description

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.

Song Dataset

The first dataset is a subset of real data from the million song datasets. 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

Log Dataset

The second dataset consists of log files in JSON format generated by this event 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.

If you would like to look at the JSON data within log_data files, you will need to create a pandas dataframe to read the data. Remember to first import JSON and pandas libraries.

df = pd.read_json(filepath, lines=True)

For example,

df = pd.read_json('data/log_data/2018/11/2018-11-01-events.json', lines=True)

would read the data file 2018-11-01-events.json.

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.

sample query

DROP TABLES WE USE

songplay_table_drop = "DROP TABLE IF EXISTS songplays"

user_table_drop = "DROP TABLE IF EXISTS users"

song_table_drop = "DROP TABLE IF EXISTS songs"

artist_table_drop = "DROP TABLE IF EXISTS artists"

time_table_drop = "DROP TABLE IF EXISTS time"

CREATE TABLES

WE USE LIKE

SONGPLAY_TABLE_CREATE = "CREATE TABLE IF NOT EXISTS SONGPLAYS(PARTS OF TABLE)"

FOR CREATE_TABLES.PY

import psycopg2

from sql_queries import create_table_queries, drop_table_queries

COMMAND USE FOR RUN CREATE_TABLES.PY

%RUN FILE OR PYTHON FILE

python create_tables.py in terminal

FOR ETL PROCESS

IN ETL.IYPNB AND ETL.PY WE USING THIS COMMANDS

import os

import glob

import psycopg2

import pandas as pd

from sql_queries import *

import json

then test.ipynb for check all queries

sample query