The Data Quality and Validation Monitor is a Big Data project that combines Data quality checks and Data extraction, built with Docker. The project follows a clean, modular pipeline where data ingestion, processing, and presentation stay separated, making it easy to test, extend, and present. It showcases practical Big Data techniques while producing a working, demo-ready application.
Traditional tools struggle to handle the volume, velocity, and variety of this data, making analysis slow and expensive. Without a Big Data approach built on Data extraction and Docker, users cannot process and analyze large datasets efficiently, and there is no scalable way to derive timely insights.
This project applies Big Data techniques through Data quality checks, orchestrated with Docker and Data extraction. The pipeline is designed for scale and reliability, with ingestion, processing, and clear evaluation. It produces consistent, reusable results and can be adapted to related large-scale tasks with minimal changes.
Docker
Apache Airflow
Python / Java
Distributed storage and processing
SQL and NoSQL stores
Logging and monitoring
Python
SQL
Modular data pipeline around Data quality checks and Data extraction
Configurable processing and storage settings
Clear logging, metrics, and error handling
Clean interface for viewing results
Reusable components for related Big Data tasks
Scalable to larger datasets
The project is layered: the ingestion layer loads and validates data through Data quality checks; the processing layer applies Big Data tools with Docker and Data extraction; and the output layer formats and presents results via Data loading. Shared configuration, logging, and monitoring modules support all layers, keeping the system robust and easy to extend.
Set up the environment, cluster, and configuration files.
Build the data ingestion and preprocessing layer with Data quality checks.
Implement the core Big Data pipeline using Docker and Data extraction.
Add the output and presentation layer via Data loading.
Wire up end-to-end flows and add error handling and logging.
Run on realistic data, tune parameters, and evaluate results.
Package the project, document it, and prepare the demo and viva report.
Build production-style Big Data applications
Apply Designing ETL pipelines and Orchestration with Airflow
Process and analyze real large-scale datasets
Work with popular Big Data tools and frameworks
Present and defend a complete Big Data project in viva
Move the pipeline to cloud infrastructure
Add more data sources and streaming support
Add advanced analytics and machine learning models
Deploy with auto-scaling for larger workloads
The Data Quality and Validation Monitor delivers a complete Big Data workflow — from data ingestion and processing to analysis and presentation. It is practical, modern, and easy to explain, making it an excellent final year project that demonstrates in-demand Big Data skills.
