GitHub topics: azuresynapse

Repositories

kevchant/AzureDevOps-SynpaseServerlessSQLPool-dacpac

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

Size: 24.4 KB - Last synced at: 7 days ago - Pushed at: about 2 years ago - Stars: 3 - Forks: 1

egoliveira1/NYC-payroll-project

Create high-quality data pipelines using Azure

Size: 682 KB - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 1 - Forks: 0

NandanHegde15/Azure-DataFactory-Generic-Pipelines

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

Size: 774 KB - Last synced at: 2 months ago - Pushed at: 2 months ago - Stars: 10 - Forks: 4

keval72/Tokyo-Olympics-Data-Analytics-Using-Azure

Explore the Tokyo Olympics data journey! We ingested a CSV from GitHub into Azure using Data Factory, stored it in Data Lake Storage Gen2, performed transformations in Databricks, conducted advanced analytics in Azure Synapse, and visualized insights using Synapse or Power BI.

Language: Jupyter Notebook - Size: 1.08 MB - Last synced at: 3 months ago - Pushed at: 3 months ago - Stars: 0 - Forks: 0

amanjot-git/AzureSQL

Learning Azure Data Fundamentals of Relational and Non Relational Data with Microsoft Learn Platform

Language: Jupyter Notebook - Size: 35.2 KB - Last synced at: 3 months ago - Pushed at: 3 months ago - Stars: 0 - Forks: 0

MohamedIMAbdelal/BookingPredictor

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

Language: Python - Size: 36.3 MB - Last synced at: 7 months ago - Pushed at: 7 months ago - Stars: 1 - Forks: 2

nazish555/Tokyo-Olympics-Azure-Data-Engineering-Project

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

Language: Jupyter Notebook - Size: 337 KB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

sairajindroju/TokyoOlympicDataAnalytics-Azure

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.

Language: Jupyter Notebook - Size: 488 KB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 1 - Forks: 0

Shashi42/Azure-End-to-End-Sales-Data-Analytics-Pipeline

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

Language: Jupyter Notebook - Size: 501 KB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0

melvinlkl/tokyo-olympics-azure

Azure End-to-End Data Engineering Project

Size: 502 KB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0

shubhammirajkar/tokyo_olympics_de_project

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.

Language: Jupyter Notebook - Size: 1.17 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 1 - Forks: 0