END TO END DATA ENGINEERING PROJECT
An end-to-end data pipeline project using Azure to extract, transform, and visualize customer sales data using an HTTP Linked Service in Azure Data Factory. Delivers an interactive Power BI dashboard with product and sales insights.
Explore the Paris Olympics data journey! We ingested a GitHub CSV into Azure via Data Factory, stored it in Data Lake Storage Gen2, performed transformations in Databricks, conducted analytics in Azure Synapse, and visualized insights in Synapse.
This is a complete solution of Many Business Data Situation where they have data on On Prem SQL Server and they want to build a Data Warehouse but at the same time want the cost to be as low as possible. This Solution Load data incrementally via Synapse Data Ingestion, Transform the Data In Notebooks with Pyspark and then Create Lake House Database
Tokyo Olympic 2021 Analysis Using Microsoft Azure platform
Leveraging Microsoft AZURE Services , DEVELOPING a high performance ETL pipeline that extracts and transform the BikeStores data and loads it to Azure data warehouse
This is an End to End Azure Data Engineering project copying data from Rest API to Azure cloud.
This project demonstrates the end-to-end process of building a data pipeline using Azure Synapse Analytics, Azure Data Factory (ADF), Databricks, and Delta Lake to ingest, clean, transform, and store data.
This project creates an end-to-end data pipeline and interactive dashboard for analyzing mutual funds' performance using Microsoft Azure and Power BI. It leverages Azure Data Factory, Data Lake Storage, SQL Database, and Databricks to build a scalable, efficient pipeline, providing real-time insights and data-driven decision-making.
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