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An end-to-end data engineering pipeline that fetches data from Wikipedia, cleans and transforms it with Apache Airflow and saves it on Azure Data Lake. Other processing takes place on Azure Data Factory, Azure Synapse and Tableau.
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.
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
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..
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.
An end-to-end data engineering pipeline that fetches data from the BingAPI, cleans and transforms it with Azure Databricks.Sentiment Analysis is performed in AzureML and the data is visualized using Tableau.