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Pulling Resource Graph Data in Azure Synapse

Description

This repository is a demonstration of a path to creating custom reporting from the Azure Resource Graph.

Disclaimer

This repository is for EDUCATION PURPOSES ONLY. It is not endorsed by Microsoft. It is only a Proof of Concept and not best practice prescription.

Tools Used

  • Azure Resource Graph REST API - Allows for sending a query to the Resource Graph and receiving a paged response back for consumption in downstream processing.
  • Azure Synapse Analytics - Synapse is a comprehensive analytics platform supporting a wide array of use cases with a diverse set of analytics engines. For this example, we'll be using two of those engines to balance both simplicity and cost.
    • Pipelines - Orchestration for data movement. In this case, call the API, if there are additional pages then repeat the call, and drop the data to a storage destination in JSON format.
    • Serverless SQL - Pick up data from the Storage Location defined in the Pipelines and transform it to a meaningful structure for reporting.
  • Azure Data Lake Storage Gen 2 - Data transferred from the Synapse Pipelines will land in Data Lake Storage to be used with Synapse Serverless SQL.
  • Power BI - Used to visualize and share data from the Serverless SQL queries.

Pre-Requisites

  1. Owner access to an Azure Resource Group.
  2. Access to a user that can grant Role-based permissions at the Azure Subscription level
  3. Ensure the below Resource Providers are Registered
    • Microsoft.Synapse
    • Microsoft.Sql

Steps

  1. Deploy Synapse to your Resource Group by clicking the button below.

    Deploy to Azure

  2. Grant Synapse's Managed Identity access to the Storage Account that deployed with it by following the steps in this link.

  3. Grant Synapse the "Reader" role for the Subscriptions you want to build reporting against.

  4. Set Up Linked Services in Synapse

  5. Import pipeline from template and Publish

  6. Set Variables and Run Pipeline

  7. Debug SQL

  8. Optional: Try the notebook way

    • Deploy spark pool
    • change variables in notebook

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