Data Pipeline Automation
Project Overview
This project involved developing an automated data pipeline for collecting, processing, and analyzing environmental monitoring data from multiple sources. The system needed to handle various data formats, perform quality control checks, and generate standardized outputs for further analysis.
Technical Approach
I developed a modular Python application using pandas for data manipulation, SQLAlchemy for database interactions, and Luigi for workflow management. The system was designed to run on a scheduled basis, pulling data from APIs, CSV files, and database sources.
Key Features
The pipeline included automated data validation, error handling with notification systems, data transformation modules, and output generation in multiple formats (CSV, GeoJSON, and database tables). A dashboard was created to monitor pipeline performance and data quality metrics.
Results and Impact
The automated pipeline reduced data processing time by 85% compared to the previous manual process, while improving data quality through consistent validation procedures. The system now processes data from over 200 monitoring stations daily and provides reliable inputs for environmental analysis and reporting.
Project Gallery
High-level architecture of the data pipeline system
Detailed data flow through the pipeline components
Python module structure and dependencies
Python code for the core ETL process
Implementation of data validation rules
Configuration for the automated scheduling system
Monitoring dashboard showing pipeline performance
Performance comparison before and after implementation
Sample data quality report generated by the system
Architecture
High-level architecture of the data pipeline system
Detailed data flow through the pipeline components
Python module structure and dependencies
Implementation
Python code for the core ETL process
Implementation of data validation rules
Configuration for the automated scheduling system
Results
Monitoring dashboard showing pipeline performance
Performance comparison before and after implementation
Sample data quality report generated by the system