data-stack-dagster-dbt-superset

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data-stack-dagster-dbt-superset

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# Data Stack

This application is an ETL pipeline for an Ecommerce selling.

# Architecture
![Dagit](https://raw.githubusercontent.com/Shindora/data-stack-dagster-dbt-superset/main/resources/architecture.png "Architecture")

## Stack

- Dagster
- Docker & Docker-Compose
- Postgres
- dbt core
- Superset

### System requirements
* [Docker](https://docs.docker.com/engine/install/)
* [Docker-Compose](https://docs.docker.com/compose/install/)

## Setup

1. With **Docker engine** and **Docker-Compose** installed, change directory to the root folder of the project (also the one that contains docker-compose.yml) and run

`docker-compose --env-file .env up --build`

2. Once the Docker suite has finished loading, open up [Dagster (dagit)](http://localhost:3000) , go to `Assets`, select all and click `Materialize selected`

![Dagit](https://raw.githubusercontent.com/Shindora/data-stack-dagster-dbt-superset/main/resources/dagit.png "Dagit")

5. When the assets have been materialized, you can open the [Superset interface](http://localhost:8088)

### Demo Credentials

Demo credentials are set in the .env file mentioned above.

### Ports exposed locally
* Dagster (dagit): 3000
* Superset: 8088
* PosgreSQL OLTP Database instance: 5432

Raw files (CSV) are saved in the **shared** folder.

The data is fictional and automatically generated. Any similarities with existing persons, entities, products or businesses are purely coincidental.

### General flow

1. Generate test data (flat files + OLTP data) using Python
2. Import flat file data and OLTP data to staging area in the Data Warehouse (Postgres), orchestrated by Dagster
3. Model data, build fact, dimension tables, and business insights load the Data Warehouse using dbt
4. Analyze and visually explore the data using Superset or directly query the Data Warehouse database instance

For superset, the default credentials are: user = admin, password = admin

## Overview of architecture

The docker-compose process will begin building the application suite. The suite is made up of the following components, each within its own docker container:
* **ingestor**: this is a collection of Python scripts that will insert data to Postgres DB
* **oltp**: this is the PostgreSQL instance that will simulate our transactional database, serving as one of the sources of the data; this is locally available on the host machine exposed on port 5432.
* **dagster_dbt**: this is the orchestrator tool that will trigger the ETL tasks; its GUI is locally available on port 3000; 
* **superset**: this contains the web-based Business Intelligence application we will use to explore the data; exposed on port 8088.

Once the docker-compose building process has completed, we may open the Dagster (dagit) GUI (locally: localhost:3000) to view the orchestration of our tasks.

![Dagster](https://raw.githubusercontent.com/Shindora/data-stack-dagster-dbt-superset/main/resources/orchestration.png "Orchestration with Dagster")

After the DAGs have completed you can either analyze the data using the querying and visualization tools provided by Superset (available locally on port 8088), or query the Data Warehouse

![Apache Superset](https://raw.githubusercontent.com/Shindora/data-stack-dagster-dbt-superset/main/resources/superset.png "Superset")

data-stack-dagster-dbt-superset

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