This project focuses on building a data validation and cleaning pipeline for a large structured dataset. The goal is to demonstrate how common data quality issues can be identified, handled, and documented in a reproducible way when working with high-volume data.
The project uses a synthetic dataset to simulate sensor or experiment log data and follows a step-by-step approach similar to real-world data pipelines.
The dataset contains over one million rows and includes fields such as record identifiers, sensor identifiers, timestamps, numeric values, and status labels. Data quality issues were intentionally introduced, including missing values, invalid numeric readings, duplicate records, and inconsistent categorical values.
The pipeline follows a clear and structured process:
- Generation of a large raw dataset
- Definition of explicit validation rules before cleaning
- Detection of duplicate records, missing values, and invalid measurements
- Standardization and validation of categorical fields
- Removal of invalid and inconsistent records based on defined rules
- Verification of data quality after each cleaning step
Each step is documented to make the reasoning behind cleaning decisions clear and easy to follow.
- Python
- NumPy
- Pandas
- Jupyter Notebook
- Git
The full pipeline is implemented in a Jupyter notebook that can be run from start to finish. All cleaning and validation steps are applied in a fixed order, and the final cleaned dataset is saved separately for downstream use.
The result of this project is a clean and validated dataset along with a documented workflow that demonstrates how to handle large-scale data quality issues in a structured and reproducible manner. The project reflects practical data engineering and validation practices relevant to data-intensive environments.
## Project Structure
large-scale-data-validation/
├── data/
│ ├── raw/
│ └── processed/
├── notebooks/
│ └── pipeline.ipynb
├── src/
├── README.md
└── requirements.txt