Safe Biking in Paris: Modern Data Engineering Pipeline
Type : application
Thème : transport_and_mobility

This project presents a **production-ready data engineering pipeline** for analyzing bicycle safety in France, particularly in Île-de-France and Paris, using real-world datasets.

By setting up this data engineering and analysis pipeline, you will get:

* Clean, analysis-ready datasets in BigQuery
* Automated EDA reports with key insights and visualizations
* ML-ready features for predictive modeling

### Data Engineering Stack:

* **Infrastructure as Code**: Terraform for reproducible cloud environments
* **Containerized Pipeline**: Docker for consistent deployment across environments
* **Data Transformation**: dbt with version control and built-in testing
* **Cloud-Native Architecture**: Google Cloud Platform (BigQuery + GCS)

### Data Quality Issues:

For those looking to reuse this dataset, the project addresses and corrects several data quality issues, including:

* **Inconsistent File Naming**: e.g., `carcteristiques-2021.csv` contains spelling errors
* **Join Complexity**: Requires choosing a granularity (e.g., one row per accident vs. per user)
* **Data Quality Fixes**:

  * Mixed data types (e.g., latitude column contains strings)
  * Non-breaking spaces disrupting numeric parsing
  * Cross-year schema changes (e.g., vehicle identifiers changed format from 2020 to 2021)
  * Inconsistent time formats (e.g., 4-character time codes with invalid patterns)
  * …