Scalable data engineering pipeline built for clinical data. Uses Terraform, DLT, GCP (GCS + BigQuery), dbt, and Dagster to ingest, transform, and orchestrate COVID-19 patient data for analytics, visualization, and future ML applications.

End-to-end Azure Data Engineering pipeline using ADF, Databricks (PySpark), ADLS Gen2, Azure SQL, and Power BI for COVID-19 analytics

End-to-end real-time data engineering pipeline using Kafka, Spark, HDFS, MongoDB, and Neo4j for COVID-19 outbreak monitoring and analytics.

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End-to-end Azure Data Engineering project using ADF, ADLS Gen2, Synapse Analytics, and Power BI. The pipeline ingests, processes, and visualizes COVID-19 data with interactive dashboards. Built with cloud-based modern data tools and real-time transformation.

This project demonstrates an end-to-end Azure Data Engineering pipeline built to ingest, transform, store, and analyze COVID-19 data for reporting and analytics use cases. The solution follows industry best practices by separating raw and curated data layers, using scalable cloud services, and enabling downstream consumption for BI analytics andml

Built an end-to-end ETL pipeline to ingest, clean, transform, and analyze global COVID-19 time-series data using Python, Pandas, and PostgreSQL. The project performs data cleaning, feature engineering, SQL storage, and analytical queries with visualization of key trends.