Examples of Flink on Azure
This project builds an End-to-End Azure Data Engineering Pipeline, performing ETL and Analytics Reporting on the AdventureWorks2022LT Database.
Azure-based solution for ingesting and analyzing Formula 1 data using Azure Data Lake Storage Gen2 and Databricks
Collection of data on Formula One Racing
Foundation Workspace for Airflow, Spark, Hive, and Azure Data Lake Gen2 via Docker
Ingested Tokyo Olympic data into Azure Data Lake using Azure Data Factory. Enhanced data quality with Apache Spark on Azure Databricks. Optimized SQL queries on Synapse Analytics, reducing execution time. Developed engaging Power BI dashboards, boosting user engagement creating KPI's with DAX.
End-to-End Azure Data Engineering Project demonstrates the implementation of a full-scale, scalable data engineering pipeline leveraging Azure cloud technologies. The project follows the Medallion Architecture to process and transform large volumes of data from multiple sources such as CSV, XLSX, JSON, PostgreSQL, SQL databases, and APIs.
The data engineering project aims to migrate a company's on-premises database to Azure, leveraging Azure Data Factory for data ingestion, transformation, and storage. The project will implement a three-stage storage strategy, consisting of bronze, silver, and gold data layers (Medalion architecture). Documentation of the project is in PDF file.
An end to End Implementation of the data lakehouse architecture
End-to-end Azure data engineering pipeline for Spotify data using Azure Data Factory, Databricks, PySpark, and Delta Live Tables with a medallion architecture.
This project presents a scalable end-to-end data pipeline designed for processing and analysing car sales data using the Azure Cloud and Databricks ecosystem.
This project builds an End-to-End Azure Data Engineering Pipeline, performing ETL and Analytics Reporting on the AdventureWorks2022LT Database.
End-to-End Azure Data Engineering Lakehouse Project using Azure Data Factory, ADLS Gen2, Databricks, Delta Lake and Unity Catalog.
END TO END DATA ENGINEERING PROJECT
Azure End-to-End Data Engineering Project using Azure Data Factory, Azure Data Lake Storage, and Medallion Architecture for scalable ETL pipelines.
Implemented an end-to-end Wealth Analytics Data Platform using Azure Data Factory, Azure SQL Database, and Azure Data Lake Storage Gen2.
Batch ELT pipeline that ingests raw CRM sales data, transforms it in Spark, and serves it to a Power BI dashboard — built on the Azure free-tier in a one-week sprint.
Ingested earthquake event data from the US Geological Survey (USGS) API for daily intervals and stored raw JSON files in the Bronze layer (Azure Data Lake Storage– ADLS).
This end-to-end data engineering project demonstrates how to design, build, and orchestrate a complete modern data platform solution using Azure Data Factory, Azure DevOps, and Azure Data Lake Storage Gen2 — following the medallion architecture (Bronze → Silver → Gold).
An Internship Project Body Fat Estimator Deployed on Azure Cloud Platform
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