README.md

Scalable-Data-Warehouse

• Data Engineering: Data warehouse tech stack with MySQL, DBT, Airflow
  • About
  • Objectives
  • Data
  • Repository overview
  • Contrbutors
  • License

About

A city traffic department wants to collect traffic data using swarm UAVs (drones) from a number of locations in the city and use the data collected for improving traffic flow in the city and for a number of other undisclosed projects.

Objectives

• Your startup is responsible for creating a scalable data warehouse that will host the vehicle trajectory data extracted by analysing footage taken by swarm drones and static roadside cameras.
• The data warehouse should take into account future needs, organise data such that a number of downstream projects query the data efficiently.

Data

• In Downloads – pNEUMA | open-traffic (epfl.ch) you can find a pNEUMA data: pNEUMA is an open large-scale dataset of naturalistic trajectories of half a million vehicles that have been collected by a one-of-a-kind experiment by a swarm of drones in the congested downtown area of Athens, Greece. Each file for a single (area, date, time) is ~87MB data.

Repository overview

Structure of the repository:

    ├── models  (contains trained model)
    ├── .github (github workflows for CI/CD, CML)
    ├── screenshots (Airflow DAG screenshots)
    ├── data (contains data versioning metedata)
    ├── scripts (contains the main script)	
    │   ├── logger.py (logger for the project)
    │   ├── preprocessing.py (dataset preprocessing)
    ├── notebooks	
    │   ├── trajectory_data_EDA.ipynb
    ├── tests 
    │   ├── test_preprocessing.py (test for the preprocessing script)
    ├── README.md (contains the project description)
    ├── requirements.txt (contains the required packages)
    |── LICENSE (license of the project)
    └── .dvc (contains the dvc configuration)

Contrbutor(s)

• Yohans Samuel

License

MIT