page_type	languages	products	description
sample	python C# TypeScript bicep	Azure Azure-Data-factory Azure-Databricks Azure-Stream-Analytics Azure-Data-Lake-Gen2 Azure-Functions	Code samples showcasing how to apply DevOps concepts to the Modern Data Warehouse Architecture leveraging different Azure Data Technologies.

DataOps for the Modern Data Warehouse

This repository contains numerous code samples and artifacts on how to apply DevOps principles to data pipelines built according to the Modern Data Warehouse (MDW) architectural pattern on Microsoft Azure.

The samples are either focused on a single azure service (Single Tech Samples) or showcases an end to end data pipeline solution as a reference implementation (End to End Samples). Each sample contains code and artifacts relating one or more of the following

• Infrastructure as Code (IaC)
• Build and Release Pipelines (CI/CD)
• Testing
• Observability / Monitoring

Single Technology Samples

• Azure SQL
  • CI/CD - AzureSQL
• Data Factory
  • CI/CD - ADF
• Azure Synapse Analytics
• Azure Databricks
  • IaC - Basic Azure Databricks deployment
  • IaC - Enterprise Security and Data Exfiltration Protection Deployment
  • IaC - Cluster Provisioning and Secure Data Access
• Stream Analytics
• Azure Purview
  • IaC - Azure Purview

End to End samples

• Parking Sensor Solution - This demonstrates batch, end-to-end data pipeline following the MDW architecture, along with a corresponding CI/CD process. This has two version of the solution:
  • Azure Data Factory and Azure Databricks Version
  • Azure Synapse Version
• Temperature Events Solution - This demonstrate a high-scale event-driven data pipeline with a focus on how to implement Observability and Load Testing.
• Dataset Versioning Solution - This demonstrates how to use DataFactory to Orchestrate DataFlow, to do DeltaLoads into DeltaLake On DataLake(DoDDDoD).
• MDW Data Governance and PII data detection - This sample demonstrates how to deploy the Infrastructure of an end-to-end MDW Pipeline using Azure DevOps pipelines along with a focus around Data Governance and PII data detection.
  • Technology stack: Azure DevOps, Azure Data Factory, Azure Databricks, Azure Purview, Presidio

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.