Local SQL Database ---> Azure ---> Power BI

Generic Pipelines / Templates for Data Factory / Synapse Pipelines w.r.t Different MSFT Offering Integrations / Use Cases

Allows you to create a dacpac for an Azure Synapse Analytics serverless SQL Pool using Azure DevOps

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 aims to predict customer booking behaviors by classifying them into three categories: Booked and Canceled Booked and Checked Out Booked and Did Not Show

Transform data from on-premises SQL Server to Azure Delta Lake Storage for Analytics and Visualization

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

Create high-quality data pipelines using Azure

Practice with Azure Synapse Analytics/Databricks Pipeline

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.

A spark reader to read the Common Event Format (CEF) built using Scala and SBT and optimised for Databricks workloads

Azure_Synapse_Project_NYC_TAXI_DATA--Sayantan Barat

Contains solutions/versions of Batch Data Pipelines created on Azure Data Factory

This Python-based project extracts data from Wikipedia using Apache Airflow, cleans it and pushes it Azure Data Lake for processing and further processing and visualization is done on Azure Data Factory, Azure Synapse and Tableau.

The repo simulates query load, optimizes performance, and offers practical guidance for building data warehouses with Azure Synapse Analytics. 🚀

This is a repository to demonstrate my details, skills, projects and to keep track of my progression in Data Analytics and Artificial Intelligence topics.

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

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.