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https://github.com/ajschofield/de-project-bentley

A project demonstrating an ETL pipeline primarily using AWS infrastructure into a data warehouse
https://github.com/ajschofield/de-project-bentley

aws eventbridge lambda northcoders python rds-postgres step-functions terraform

Last synced: 25 days ago
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A project demonstrating an ETL pipeline primarily using AWS infrastructure into a data warehouse

Host: GitHub
URL: https://github.com/ajschofield/de-project-bentley
Owner: ajschofield
Archived: true
Created: 2024-08-12T08:36:45.000Z (6 months ago)
Default Branch: main
Last Pushed: 2024-09-03T15:25:03.000Z (5 months ago)
Last Synced: 2024-09-28T13:21:31.142Z (5 months ago)
Topics: aws, eventbridge, lambda, northcoders, python, rds-postgres, step-functions, terraform
Language: Python
Homepage:
Size: 372 KB
Stars: 2
Watchers: 1
Forks: 4
Open Issues: 4
Metadata Files:
- Readme: README.md

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README

        > [!NOTE]

> Considering that myself and my team have graduated from the Northcoders Data Engineering course, this project will be archived and made read-only.

> I will be continuing this project solo, which you can find [here](https://github.com/ajschofield/ETL-Project), where I will be adding more features

> over time.

# ToteSys - Data Engineering Project

[![Python](https://img.shields.io/badge/Python-FFD43B?style=for-the-badge&logo=python&logoColor=blue)](https://www.python.org/)

[![AWS](https://img.shields.io/badge/Amazon_AWS-FF9900?style=for-the-badge&logo=amazonaws&logoColor=white)](https://aws.amazon.com/)

[![Terraform](https://img.shields.io/badge/Terraform-7B42BC?style=for-the-badge&logo=terraform&logoColor=white)](https://www.terraform.io/)

[![Postgresql](https://img.shields.io/badge/PostgreSQL-316192?style=for-the-badge&logo=postgresql&logoColor=white)](https://www.postgresql.org/)

[![GitHub Actions](https://img.shields.io/badge/GitHub_Actions-2088FF?style=for-the-badge&logo=github-actions&logoColor=white)](https://github.com/features/actions)

[![Terraform Main Deployment Workflow Status](https://img.shields.io/github/actions/workflow/status/ajschofield/de-project-bentley/deploy.yml?branch=main&style=flat-square&label=deploy)](https://github.com/ajschofield/de-project-bentley/actions/workflows/deploy.yml?query=branch%3Amain)

[![Production Environment Status](https://img.shields.io/github/deployments/ajschofield/de-project-bentley/production?style=flat-square&label=env)](https://github.com/ajschofield/de-project-bentley/deployments/production)

# Contributors

  

    

      

        

        


        _{Ellie Symonds}

      

    

    

      

        

        


        _{Lianmei Manon-og}

      

    

    

      

        

        


        _{Tolu Ajibade}

      

    

  

  

    

      

        

        


        _{Joslin Rashleigh}

      

    

    

      

        

        


        _{Anzelika Belotelova}

      

    

    

      

        

        


        _{Alex Schofield}

      

    

  

# Summary

The project aims to implement a data platform that can extract data from an

operational database, archive it in a data lake, and make it easily accessible

within a remodelled OLAP data warehouse.

The solution showcases our skills in:

- Python

- PostgreSQL

- Database modelling

- Amazon Web Services (AWS)

- Agile methodologies

# Main Objectives

Our goal is to create a reliable ETL (Extract, Transform, Load) pipeline that

can:

1. Extract the data from the `totesys` operational database

2. Store the data in AWS S3 buckets, that will form our data lake

3. Transform the data into a suitable schema for the data warehouse

4. Load the transformed data into the data warehouse hosted on AWS

# Key Features

We aim for the project to have certain features. Some are more prioritised than

others.

- Automated data ingestion from `totesys` db

- Data storage for ingested and processed data in S3 buckets

- Data transformation for data warehouse schema

- Automated data loading into the data warehouse schema

- Logging and monitoring with CloudWatch

- Notifications for errors and successful runs (e.g. successful ingestion)

- Visualisation of warehouse data