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azure-data-lake-gen2

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This project builds an End-to-End Azure Data Engineering Pipeline, performing ETL and Analytics Reporting on the AdventureWorks2017LT Database.

  • Updated Jul 27, 2024
  • Jupyter Notebook

A comprehensive ETL pipeline and sales analysis project leveraging Microsoft Azure and PySpark, designed to optimize e-commerce sales by providing actionable insights through detailed data analysis.

  • Updated Mar 22, 2024
  • Jupyter Notebook

This project builds an End-to-End Azure Data Engineering Pipeline, performing ETL and Analytics Reporting on the AdventureWorks2022LT Database.

  • Updated Jan 24, 2024
  • Jupyter Notebook

Data Engineering Project - Python, PySpark & SQL - Azure Data Factory (ADF), DataBricks, Synapse Analytics, Azure Data Lake Storage (ADLS) Gen2, Power BI, Tableau and Looker Studio

  • Updated Oct 13, 2023
  • Jupyter Notebook

This project demonstrates an ETL pipeline using Microsoft Azure for IMDb Movie Rating Dataset analysis. It covers data extraction from Azure Blob Storage, transformation with Azure Databricks, and loading into Azure SQL using Azure Data Factory. The pipeline automates insights generation and is a practical example of cloud-based data engineering.

  • Updated Mar 22, 2024
  • Jupyter Notebook

A cutting-edge data project leverages Azure's suite of services to seamlessly transform raw data from GitHub into actionable insights. Using Azure Data Factory for data ingestion, Databricks for PySpark transformations, Synapse Analytics for advanced analysis, and Power BI for intuitive visualization, this project navigates complex data workflows..

  • Updated May 27, 2024
  • Jupyter Notebook

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