A scalable datawarehouse built using Airflow, DBT, Redash, and Postgres
An ELT pipeline that can be used to process data from a variety of sources. The pipeline is designed to be scalable and easy to use. The pipeline is designed to be used in a variety of environments like Data Warehouse. It can be used by analytic engineers in the different department setup transformation workflows on a need basis.
After joining an AI startup that deploys sensors to businesses, collects data from all activities in a business - people’s interaction, traffic flows, smart appliances installed in a company, the objective is to help organisations obtain critical intelligence based on public and private data they collect and organise.
The Data used for this project is from open source dataset called PNeuma which 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.
Structure of the repository:
├── dags (airflow scripts containing the dags)
├── .github (github workflows for CI/CD, CML)
├── screenshots (Important screenshots)
├── data (contains data)
├── redash (contains the redash configuration)
├── scripts (contains the main script)
│ ├── logger.py (logger for the project)
│ ├── loader.py (Data loading)
├── notebooks
│ ├── EDA.ipynb (overview of the Data)
├── tests
│ ├── test_scripts.py (test for the script)
├── README.md (contains the project description)
├── requirements.txt (contains the required packages)
|── LICENSE (license of the project)
├── setup.py (contains the setup of the project)
└── .dvc (contains the dvc configuration)