# Project 3: Song Play Analysis with S3 and Redshift
-------------------------

### Introduction

In this project, we will help one music streaming startup - Sparkify, to move their user and song database processes to the cloud. To reach that, we build an ETL pipeline to extracts their data from **AWS S3** (data storage), stages tables on **AWS Redshift** (data warehouse with *columnar storage*), and execute **SQL** statements to create the analytics tables from these staging tables.

### Datasets
Datasets used in this project are provided in two public **S3 buckets**. 

+ **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 file paths to two files in this dataset.

```
song_data/A/B/C/TRABCEI128F424C983.json
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}

```

+ **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 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 file paths 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.

![log-data](./log-data.PNG)

The **Redshift** service is where data will be ingested and transformed, using `COPY` command we will access to the JSON files inside the buckets and copy their content to our *staging tables*.

### Database Schema
We have two staging tables which *copy* the JSON file inside the **S3 buckets**.
#### Staging Tables
+ **staging_songs** - info about songs and artists
+ **staging_events** - actions done by users (which song are listening, etc.. )


A star schema was designed to optimize queries on song play analysis. This includes the following tables.

#### Fact Table 
+ **songplays** - records in event data associated with song plays i.e. records with page `NextSong`

#### Dimension Tables
+ **users** - users in the app
+ **songs** - songs in music database
+ **artists** - artists in music database
+ **time** - timestamps of records in **songplays** broken down into specific units


The database schema is shown as follows

![schema](./schema_diagram.PNG)

### Data Warehouse Configurations and Setup steps:
* Create a new `IAM user` in your AWS account
* Give it AdministratorAccess and Attach policies
* Use access key and secret key to create clients for `EC2`, `S3`, `IAM`, and `Redshift`.
* Create an `IAM Role` that makes `Redshift` able to access `S3 bucket` (ReadOnly)
* Create a `RedShift Cluster` and get the `DWH_ENDPOIN(Host address)` and `DWH_ROLE_ARN` and fill the config file.

### ETL Pipeline
+ Created tables to store the data from `S3 buckets`.
+ Loading the data from `S3 buckets` to staging tables in the `Redshift Cluster`.
+ Inserted data into fact and dimension tables from the staging tables.

### Project Structure

+ `create_tables.py` - This script will drop old tables (if exist) ad re-create new tables.
+ `etl.py` - This script executes the queries that extract `JSON` data from the `S3 bucket` and ingest them to `Redshift`.
+ `sql_queries.py` - This file contains variables with SQL statement in String formats, partitioned by `CREATE`, `DROP`, `COPY` and `INSERT` statement.
+ `dhw.cfg` - Configuration file used that contains info about `Redshift`, `IAM` and `S3`

### How to Run

1. Create tables by running `create_tables.py`.

2. Execute ETL process by running `etl.py`.