🎯 The Problem
Refonte Learning needed efficient data processing capabilities to handle large-scale educational data from multiple sources. The existing manual processes were time-consuming, error-prone, and couldn't scale with the growing data volume. There was also a need for real-time analytics and machine learning model integration.
💡 The Solution
Developed comprehensive ETL pipelines using Python and Apache Airflow for automated data processing. Implemented database optimization strategies for PostgreSQL to improve query performance. Created cloud-based data warehouses on AWS (SageMaker, Lambda) and built interactive dashboards using Streamlit. Integrated ML models into real-time analytics pipelines using Spark for distributed processing.
🚀 The Outcome
Successfully improved data processing efficiency and enabled real-time analytics for educational insights. The automated pipelines handle large-scale data processing with CI/CD deployment. Deployed ML models into production analytics systems, supporting data-driven decision making and enabling personalized learning recommendations for students.
Project Visuals
Check out the GitHub repository for code samples, demos, and detailed implementation notes.
Source Code
Available on GitHub
Documentation
README & Guides