An interactive showcase of data parsing, distributed databases, exploratory analysis pipelines, and federated data architecture.
A deep dive into managing data at scale — from raw ingestion to distributed intelligence.
Mastering heterogeneous data ingestion — CSV, JSON, XML, binary formats, HTML scraping, and SQL/NoSQL CRUD operations with Python.
Understanding distributed query processing, schema heterogeneity, mediator-wrapper patterns, and CAP theorem tradeoffs.
Pre-processing pipelines with feature engineering, outlier detection, correlation heatmaps on Indian water resource data.
Applying Linear Regression for predictive modeling with StandardScaler, train-test splits, and performance metrics (MSE, R²).
Click on an assignment card to explore the full report, code, and experimental outputs.
End-to-End Data Management
A comprehensive lab covering data parsing across multiple formats, NoSQL/SQL CRUD, EDA with visualization, and a Linear Regression ML model on Indian Water Resource data.
Academic Research Report
A professional book-format chapter covering distributed query processing, schema heterogeneity resolution, mediator-wrapper architecture, 2PC/Saga transactions, CAP theorem, security & real-world case studies.
MapReduce & Analytics
Hadoop cluster deployment and distributed data processing on quality datasets using MapReduce and charting visualizations.
Federated Data Management 2.0
Advanced research on federated learning applied to management ecosystems, privacy-preserving techniques (FedAvg), and taxonomy of federated models.