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Architectural Feedback: Is SPFx + Azure Database a viable choice for an internal ticketing system?
Hi everyone, I am currently evaluating the architecture for a new interdepartmental ticketing system and would love to get your feedback, real-world experiences, or best practices regarding our proposed approach. Our goal is to centralize and streamline requests, tracking, and communication across various departments within the organization. We want to provide an intuitive interface that integrates seamlessly into our existing workplace (SharePoint Online / Microsoft Teams). Proposed Architecture Frontend / UI: Custom web parts developed using SPFx (SharePoint Framework), embedded into SharePoint pages and surfaced in Microsoft Teams. Backend / Database: Azure Database for PostgreSQL for managing relational data, audit logs, complex ticket statuses, and SLA metrics. Integration Layer: Custom REST APIs (hosted via Azure Functions / App Service) to handle transactions securely between SPFx and PostgreSQL Rationale Behind This Choice User Experience: SPFx provides a native look and feel within Microsoft 365, minimizing user onboarding friction. Relational Data Complexity: We prefer PostgreSQL over standard SharePoint Lists due to data volume projections, complex relational data models, granular access control requirements per department, and advanced analytics needs. Questions Architecture Viability: Do you consider using SPFx purely as a frontend consumer for external REST APIs connected to PostgreSQL a clean and sustainable pattern long-term? SharePoint Lists / Dataverse vs. External DB: Is the added overhead of maintaining a custom API + PostgreSQL in Azure justified, or would you strongly favor Dataverse or native SharePoint Lists for this specific use case? Authentication & Security: Any specific tips when securing SPFx calls to Azure API/Functions using Microsoft Entra ID (Azure AD) to ensure proper department-level data segregation? Maintenance & Governance: Are there any major maintenance, lifecycle, or performance bottlenecks we should prepare for when maintaining a hybrid SPFx + external DB solution? Thanks in advance for any insights, lessons learned, or potential red flags!
Reliable Interactive Resources for Dp300 exam
Hello everyone, I hope you're all having a great day! I wanted to reach out and start a discussion about preparing for the DP-300 (Azure Database Administrator) certification exam. I’ve been researching various resources, but I’m struggling to find reliable and interactive materials that truly help with exam prep. For those who have already passed the DP-300, could you share any interactive and trustworthy resources you used during your study? Whether it's courses, hands-on labs, or practice exams, I’d really appreciate your recommendations. Any advice on how to effectively prepare would be incredibly helpful! Thank you so much for your time reading this discussion and for sharing your experiences!
The Role of Microsoft Azure in the Solution
Microsoft Azure plays a pivotal role in the new Backup-as-a-Service solution. As one of the leading cloud platforms globally, Azure offers unparalleled infrastructure, security, and scalability. The integration of the Backup-as-a-Service solution with Azure ensures that businesses can leverage the full power of the cloud while maintaining control over their data. Azure's global network of data centers provides a reliable and secure environment for data storage and backup. This geographical distribution also enhances disaster recovery capabilities, allowing businesses to recover data from multiple locations if needed. Additionally, Azure's advanced security features, such as encryption and identity management, further enhance the overall security of the Backup-as-a-Service solution. Addressing the Challenges of Data Protection The introduction of this new Backup-as-a-Service solution addresses several key challenges that businesses face in the realm of data protection: Complexity of Data Management: Managing large volumes of data across multiple platforms can be complex and time-consuming. The Backup-as-a-Service solution simplifies this process by centralizing data protection efforts within the Microsoft 365 ecosystem, allowing IT teams to manage backups more efficiently. Not all of solution can handle but Azure Storage from Nakivo can manage large data volumes by centralizing protection efforts across platforms. Data Compliance Requirements: With data protection regulations becoming more stringent, businesses must ensure that their data protection strategies comply with local and international laws. The solution offers built-in compliance tools that help organizations meet these requirements, reducing the risk of costly fines and penalties. Increasing Cybersecurity Threats: The rise in cyberattacks, particularly ransomware, has made data protection a top priority for businesses. The solution's advanced security features, including ransomware protection and disaster recovery, provide a robust defense against these threats. Scalability Needs: As businesses grow, so does their data. The solution's scalable architecture ensures that businesses can continue to protect their data as they expand, without needing to invest in new infrastructure or tools.
How to migrate SQL MI to different region
We created our Azure SQL MI in region US West, which does not support Zone redundancy. While we don't require it now, we may require it in the future. What options do we have now vs later to enable this? We have not gone live yet so we have the option to rebuild it entirely in a supported region, but if we decide not to do that, are there other options? I'm thinking we would set up a new Azure SQL MI in a new region and use Availability Groups to replicate the data.Ihor Zahorodnii DataOps for the modern data warehouse
Ihor Zahorodnii DataOps for the modern data warehouse This article describes how a fictional city planning office could use this solution. The solution provides an end-to-end data pipeline that follows the MDW architectural pattern, along with corresponding DevOps and DataOps processes, to assess parking use and make more informed business decisions. Architecture The following diagram shows the overall architecture of the solution. Dataflow Azure Data Factory (ADF) orchestrates and Azure Data Lake Storage (ADLS) Gen2 stores the data: The Contoso city parking web service API is available to transfer data from the parking spots. There's an ADF copy job that transfers the data into the Landing schema. Next, Azure Databricks cleanses and standardizes the data. It takes the raw data and conditions it so data scientists can use it. If validation reveals any bad data, it gets dumped into the Malformed schema. Important People have asked why the data isn't validated before it's stored in ADLS. The reason is that the validation might introduce a bug that could corrupt the dataset. If you introduce a bug at this step, you can fix the bug and replay your pipeline. If you dumped the bad data before you added it to ADLS, then the corrupted data is useless because you can't replay your pipeline. There's a second Azure Databricks transform step that converts the data into a format that you can store in the data warehouse. Finally, the pipeline serves the data in two different ways: Databricks makes the data available to the data scientist so they can train models. Polybase moves the data from the data lake to Azure Synapse Analytics and Power BI accesses the data and presents it to the business user. Components The solution uses these components: https://azure.microsoft.com/services/data-factory https://azure.microsoft.com/services/databricks https://learn.microsoft.com/en-us/azure/storage/blobs/data-lake-storage-introduction https://azure.microsoft.com/services/synapse-analytics https://azure.microsoft.com/services/key-vault https://azure.microsoft.com/services/devops https://powerbi.microsoft.com/ Scenario details A modern data warehouse (MDW) lets you easily bring all of your data together at any scale. It doesn't matter if it's structured, unstructured, or semi-structured data. You can gain insights to an MDW through analytical dashboards, operational reports, or advanced analytics for all your users. Setting up an MDW environment for both development (dev) and production (prod) environments is complex. Automating the process is key. It helps increase productivity while minimizing the risk of errors. This article describes how a fictional city planning office could use this solution. The solution provides an end-to-end data pipeline that follows the MDW architectural pattern, along with corresponding DevOps and DataOps processes, to assess parking use and make more informed business decisions. Solution requirements Ability to collect data from different sources or systems. Infrastructure as code: deploy new dev and staging (stg) environments in an automated manner. Deploy application changes across different environments in an automated manner: Implement continuous integration and continuous delivery (CI/CD) pipelines. Use deployment gates for manual approvals. Pipeline as Code: ensure the CI/CD pipeline definitions are in source control. Carry out integration tests on changes using a sample data set. Run pipelines on a scheduled basis. Support future agile development, including the addition of data science workloads. Support for both row-level and object-level security: The security feature is available in SQL Database. You can also find it in Azure Synapse Analytics, Azure Analysis Services (AAS) and Power BI. Support for 10 concurrent dashboard users and 20 concurrent power users. The data pipeline should carry out data validation and filter out malformed records to a specified store. Support monitoring. Centralized configuration in a secure storage like Azure Key Vault. More details here: https://learn.microsoft.com/en-us/azure/architecture/databases/architecture/dataops-mdw Ihor Zahorodnii Ihor ZahorodniiUnable to Create Batch Pool in Batch Account with Batch Explorer Tool
Description: I am encountering an issue while creating a batch pool using the Batch Explorer as mentioned in the msdn https://learn.microsoft.com/en-us/azure/batch/tutorial-run-python-batch-azure-data-factory. Despite following the standard procedures, I am unable to successfully create the pool. I have detailed the steps and configuration below. Issue Details: Service: Batch Account Tool: Batch Explorer Error Message: ["resizeErrors": [ { "code": "OperationsRestrictedByPolicy", "message": "Assigned policy on resource has blocked pool operation.", "values": [ { "name": "Code", "value": "OperationsOnNetworkSecurityGroupRestrictedByPolicy" }, { "name": "Reason", "value": "Policy is forbidding operations on the Network Security Group resource, which has blocked resize/create/delete of the pool. Check policy assignment." } ] }] Error Screenshot: Steps to Reproduce: https://learn.microsoft.com/en-us/azure/batch/tutorial-run-python-batch-azure-data-factory Sign in to Batch Explorer with Azure credentials. Select the Batch account. Navigate to Pools on the left sidebar, and select the + icon to add a pool. Complete the Add a pool to the account form as follows: Under ID, enter custom-activity-pool. Under Dedicated nodes, enter 2. For Select an operating system configuration, select the Data science tab, and then select Dsvm Win 2019. For Choose a virtual machine size, select Standard_F2s_v2. For Start Task, select Add a start task. On the start task screen, under Command line, enter cmd /c "pip install azure-storage-blob pandas", and then select Select. Enable network configuration with virtual network to use private endpoints.[Error is coming when we enable the network configuration with private virtual network] Select Save and close.
Extracting tenantSettings via REST API from Microsoft Fabric to SharePoint directly using PowerShell
Microsoft Fabric is a unified platform that integrates data and services, encompassing data science and data lakes, to enhance your team’s data utilization. Discover how to leverage Fabric’s features like OneLake, Data Factory, Synapse, Data Activator, Power BI, and Microsoft Purview to usher your data into the AI era. Extracting tenantSettings via REST API from Microsoft Fabric to SharePoint directly using PowerShell/CloudShell. To generate a tenant settings report using REST API, you can use the following steps: Obtain an access token from Microsoft Entra (aka Azure Active Directory) by following this guide: https://docs.microsoft.com/azure/active-directory/develop/v2-oauth2-client-creds-grant-flow Use the access token to call the Fabric REST API endpoint for tenant settings: https://learn.microsoft.com/rest/api/fabric/admin/tenants/get-tenant-settings Authenticate SharePoint Teams Site using Service Principal and Certificate/Thumbprint and stream the REST API response directly to SharePoint file. Parse the JSON response and extract the relevant information for your report. Here’s a sample PowerShell/CloudShell script that can produce a report on Microsoft Fabric (including Power BI) tenant settings using the REST API: Download complete code snippet: fabric/admin/tenant_settings_SP.ps1 (github)
