This is a repository to demonstrate my details, skills, projects and to keep track of my progression in Data Analytics and Artificial Intelligence topics.
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Updated
Oct 1, 2024
This is a repository to demonstrate my details, skills, projects and to keep track of my progression in Data Analytics and Artificial Intelligence topics.
Generic Pipelines / Templates for Data Factory / Synapse Pipelines w.r.t Different MSFT Offering Integrations / Use Cases
The repo simulates query load, optimizes performance, and offers practical guidance for building data warehouses with Azure Synapse Analytics. 🚀
Learning Azure Data Fundamentals of Relational and Non Relational Data with Microsoft Learn Platform
This project leverages Azure Cloud services like Azure Data Factory, Azure Databricks, and Synapse Analytics to execute a data engineering workflow. Utilizing data sourced from the Olympic API on GitHub, it involves extracting raw data into Azure Data Lake Storage, transforming it with PySpark on Azure Databricks, and analyzing the transformed data
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.
Implemented an end-to-end Azure data engineering solution to process Tokyo Olympics 2021 data, encompassing extraction, transformation, analytics, and visualization.
Explore the Tokyo 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 advanced analytics in Azure Synapse, and visualized insights in Synapse or Power BI.
This project builds an End-to-End Azure Data Engineering Pipeline, performing ETL and Analytics Reporting on the AdventureWorks2022LT Database.
The aim of this project is to build a cost efficient Data Warehouse on Amazon's Retail sales data and perform Customer lifetime value analyses
Create high-quality data pipelines using Azure
A spark reader to read the Common Event Format (CEF) built using Scala and SBT and optimised for Databricks workloads
Local SQL Database ---> Azure ---> Power BI
The aim of this project is to build a cost efficient Data Warehouse on Amazon's Retail sales data and perform Customer lifetime value analyses.
Azure for End to End Data Science Project
Transform data from on-premises SQL Server to Azure Delta Lake Storage for Analytics and Visualization
Allows you to create a dacpac for an Azure Synapse Analytics serverless SQL Pool using Azure DevOps
Practice with Azure Synapse Analytics/Databricks Pipeline
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