Microsoft Fabric: Revealing Cost-Saving Results from Automating Pause & Resume Fabric Capacity

Microsoft Fabric: Revealing Cost-Saving Results from Automating Pause & Resume Fabric Capacity

If you work in data and analytics, particularly within the Microsoft Data Platform, you have likely heard of Microsoft Fabric and its many capabilities. However, one of the biggest challenges organisations face is managing costs effectively.

In previous blogs and videos, I have covered how to optimise Microsoft Fabric capacity costs by automating the pause and resume process using Logic Apps. This approach ensures that your Fabric capacity runs only when needed, reducing unnecessary expenses. But how much can this method actually save? In this post, I share the real cost-saving results after applying this automation over the past few months.

Why Automate Pause & Resume?

Microsoft Fabric is a powerful platform, but maintaining capacity during off-peak hours can be costly. If your workloads are not running 24/7, you might be paying for unused capacity. By automating the pause and resume process, you can ensure that your environment is active only when required, leading to substantial savings.

When Should Organisations Consider Automation?

Not every organisation requires automated capacity management, but here are some scenarios where it makes sense:

  • Non-Continuous Workloads: If your organisation runs batch processing, reporting, or analytics workloads that do not need 24/7 uptime, automation can help cut costs.
  • Business Hours Usage: If your team primarily operates during specific hours, pausing capacity during off-hours prevents unnecessary spending.
  • Seasonal or Project-Based Needs: Companies with fluctuating workloads based on seasons or projects can optimise expenses by automating capacity scaling.
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Power BI Desktop Versions Demystified: Part 1, Power BI Desktop and Power BI Desktop RS

If you are a Power BI power user, you may have wondered: how many versions of Power BI Desktop are there? The quick answer is: it depends!

Depending on your organisation’s preferences, data governance requirements, and the platforms you intend to use for report deployment you may use either Power BI Desktop, the “standard version”, or Power BI Desktop RS (Report Server). Power BI Desktop has variations tailored to meet specific needs, such as cloud-based analytics or on-premises reporting. While many users might only encounter the standard version, there’s another important variant for specialised scenarios.

Power BI Desktop comes in two primary versions:

  1. Power BI Desktop:
    This is the standard version most users rely on. It’s the go-to tool for transforming data, creating semantic models, and building interactive reports. This version is designed to seamlessly integrate with the Power BI Service hosted on Microsoft Fabric, enabling cloud-based sharing, collaboration, and advanced features like Direct LakeAI-driven insights, and more. Regular updates ensure that this version includes the latest features and innovations, such as new Power Query and DAX functions, enhanced visuals, and cutting-edge integrations.
  1. Power BI Desktop RS (Report Server):
    This is a specialised version of Power BI Desktop designed to work exclusively with Power BI Report Server, a locally hosted reporting platform. It is tailored for organisations that prefer to keep their data and reports on-premises due to regulatory, security, or strategic reasons, avoiding reliance on cloud services like the Power BI Service on Microsoft Fabric. Although the two versions look nearly identical in functionality, they serve distinct purposes. Power BI Desktop RS is specifically aligned with the capabilities of Power BI Report Server, supporting features available up to the latest release cycle of the server. For instance, Power BI Desktop RS updates are less frequent; typically released every few months, in line with Power BI Report Server’s update schedule; making it slightly behind the standard version in terms of cutting-edge features. However, it ensures stability and compatibility for on-premises deployments.
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Microsoft Fabric: Troubleshooting Query Parameters in Published Semantic Models

Microsoft Fabric: Troubleshooting Query Parameters in Published Semantic Models

Power Query is a powerful tool within the Microsoft Fabric environment, enabling users to manage data sources and transform data efficiently. However, a common issue you may face is that after publishing the Semantic Model, the Power Query parameters either do not appear or are greyed out, making them non-editable. In this post and its accompanying YouTube video, I’ll walk you through the steps to diagnose and fix these problems, ensuring that your parameters work as expected in your published semantic models.

Why Do Power Query Parameters Become Unavailable?

There are a few reasons why your Power Query parameters might not appear or be editable after you’ve published your report to Microsoft Fabric. These issues generally relate to either the way the parameters are set up within Power Query in Power BI Desktop or how they interact with the data sources.

Common Cause and Fix

1. Parameter Data Type in Power Query

One of the most common reasons your parameters might be greyed out or non-editable is due to the parameters’ data types defined in Power Query within Power BI Desktop. If your parameters are of type any, then they won’t show up, or they are read-only (greyed out). The fixation is easy:

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Microsoft Fabric: Capacity Cost Management Part 3, Pause Capacity During Christmas with Azure Logic Apps

Microsoft Fabric: Capacity Cost Management Part 3, Pause Capacity During Christmas with Azure Logic Apps

In the first blog post of this series, I explained that we can Pause and Resume a Microsoft Fabric capacity from Azure Portal. In the second blog and its accompanying YouTube video, I showed you how to automate the Pause and Resume actions in Azure LogicApps so the capacity starts at 8:00 AM and stops at 4:00 PM. While I have already mentioned in those posts, it is worthwhile to mention again that these methods only make sense for PAYG (Pay-As-You-Go) capacities and NOT the Reservation capacities. While the method works fine, you may need more fine-tuning.

Managing operational costs becomes crucial for businesses leveraging Microsoft Fabric capacities when the holiday season approaches. This presents a unique challenge of maintaining efficiency while reducing unnecessary expenses, especially during Christmas when business operations might slow down or pause entirely.

In this post and video, I will extend the discussions from my previous blog and demonstrate how to optimise your Azure Logic Apps to manage Microsoft Fabric capacity during the Christmas holidays.

Extending the Logic Apps Workflow

Existing Setup Recap

In earlier discussions, we’ve explored using Azure Logic Apps to manage Fabric capacity effectively from 8:00 AM to 4:00 PM on regular business days and pausing operations afterwards. This setup ensures that we’re not incurring costs when the capacity isn’t needed, particularly from 4:00 PM to 8:00 AM the next morning, and throughout the weekends. I encourage you to check out my previous post for more information. This is how the existing solution looks like in Azure LogicApps:

Automating Microsoft Fabric Capacity with Azure LogicApps
Automating Microsoft Fabric Capacity with Azure LogicApps

Incorporating Holiday Schedules

The key to extending this setup for the Christmas period lies in integrating specific holiday schedules into your existing workflows using Workflow Definition Language which is used in Azure Logic Apps and Microsoft Flow. The following expression determines if the current date (in New Zealand Standard Time) falls within the period from December 25th of the current year to January 2nd of the next year:

and(
    greaterOrEquals(
        int(
            formatDateTime(
                convertFromUtc(
                    utcNow(), 
                    'New Zealand Standard Time'
                ), 
                'yyyyMMdd'
            )
        ), 
        int(
   concat(
    formatDateTime(
     utcNow()
     , 'yyyy'
     )
    , '1225'
    )
   ) 
    ), 
    lessOrEquals(
        int(
            formatDateTime(
                convertFromUtc(
                    utcNow(), 
                    'New Zealand Standard Time'
                ), 
                'yyyyMMdd'
            )
        ), 
        int(
   concat(
    add(
     int(
      formatDateTime(
       utcNow()
       , 'yyyy'
       )
      )
     ,1
     )
    , '0102'
    )
   )
  )
)

The following section explains how the expression works.

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