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Category: Cloud

Extracting Data from DAX Measures into CSV

Published 2022-06-17 by Kevin Feasel

Gilbert Quevauvilliers builds a process:

In this blog post I am going to demonstrate how to use the new Power Automate Flow to extract data from a DAX measure into a SharePoint CSV file.

I got this idea after reading the blog post from the Microsoft Power BI Team: Unlocking new self-service BI scenarios with ExecuteQueries support in Power Automate | Microsoft Power BI Blog | Microsoft Power BI

The great news is that this works on Power BI Pro, Premium Per User and Premium.

Read on to see how.

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Point-in-Time Recovery with Azure SQL DB and Managed Instances

Published 2022-06-14 by Kevin Feasel

Ahmed Mahmoud looks at point-in-time recovery and answers some frequently asked questions:

On some occasions, after the failover is initiated, the current Primary DR will start a new backup chain from that point and old backups are available on the current secondary DR. If we want to restore the backups which exists in Secondary it will not allow us to perform, apparently restore cannot be initiated on the Primary as the backup is not available.

Also, sometimes we observe in secondary DR for few databases, PitR restore point is available and for few databases it shows “no restore point available”    

Read on to understand why that happens and what you can do about it.

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Guidance on When to Use Azure Data Explorer

Published 2022-06-13 by Kevin Feasel

Tzvia Gitlin Troyna has a flow chart for us:

Azure Data Explorer is a big data interactive analytics platform that empowers people to make data driven decisions in a highly agile environment. The factors listed below can help assess if Azure Data Explorer is a good fit for the workload at hand. These are the key questions to ask yourself.

The following flowchart table summarize the key questions to ask when you’re considering using Azure Data Explorer.

In addition to the flow chart, there is a table of three common patterns of interaction which ADE can do well.

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Working with Azure VM Scale Sets

Published 2022-06-10 by Kevin Feasel

Arun Sirpal explains the benefit behind scale sets in Azure:

I really like scale sets. It lets you create and manage up to 1000 load balanced VMs per availability zone using windows or Linux images. (We can have flexible or uniforms modes for orchestration which dictates if you go down the homogenous VM route or a mix, where a mix is the flexible option.

There are many other benefits too apart from scaling, such as built-in load balancing options, increased resiliency via 3 Availability Zones and from a cost perspective you can couple scale sets with Azure Hybrid benefit or even use reserved instances – cost is important in the cloud!

Read the whole thing.

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Reconciling Tag Names across Azure

Published 2022-06-07 by Kevin Feasel

Anthony Watherston has an interesting script:

During a recent cost optimization workshop with a customer, they mentioned that although they had some tagging policies in place there was no consistency of tag names across the Azure environment. This post introduces a script to remediate this and remove some confusion from your tagging strategy.

The customer was trying to ensure that all resources were being tagged with a cost centre tag. Some of this was automatic and some was done manually by people. While there was a policy in place to control this in the future, they needed a way to remediate the existing resources.

This is really useful if you have enough information to create a to-and-from mapping. It won’t automatically understand anything, so you’ll need to do the digging but it will do the renaming.

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Making the Case for Azure Analysis Services

Published 2022-06-06 by Kevin Feasel

Teo Lachev makes the case:

Microsoft BI practitioners have three options for hosting semantic models: SSAS (on prem), Azure Analysis Services (cloud), and Power BI (cloud). AAS is somewhat caught between a rock and a hard place. Given that Power BI gets the most attention for cloud deployment, why would you consider AAS at all? There are two main reasons:

Read on for the reasons. Knowing how much it does cost, it almost feels like trying to thread a needle: if you don’t spent enough or have enough data, Power BI is typically much more efficient; if you have sufficient data, I’d want to do a proper cost analysis between on-prem (or IaaS) Analysis Services and Azure Analysis Services.

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Cancelling a Cosmos DB Query

Published 2022-06-02 by Kevin Feasel

Hasan Savran pushes the big red button:

Sometimes you go to a website and the page does not want to open for whatever reason. You might just click Stop and move on to another page. The stop button does not really stop the request, the server still tries to complete the request and send a response to the client even client does not exist anymore. Rather than clicking on Stop maybe you click Refresh which triggers another request when the first one is not completed yet.

      This scenario applies to all database calls. It might take longer to run a query for a reason and simply you need to wait until you get a response from the database server. If it takes too much time, you might want to cancel the request and try to look at your query to make it faster. You can do that programmatically by using CancellationTokens.

Read on for more information about cancellation tokens and how you can use them.

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Consuming an Azure ML AutoML Model in Excel

Published 2022-06-01 by Kevin Feasel

Lewis Prince needs to do some heavy lifting in Excel:

It has come back to my turn to write a blog post, and if you remember my previous one concerned why you should use Azure based AutoMl and subsequently how to do so. If you followed that then you will be left with a model of which you’ve scored and know the performance of, but no way of how to then deploy and use your model. I will outline the steps needed to do this (which involves a major shortcut as we are using an AutoMl model), and then show you the required VBA needed to consume this in Microsoft Excel.

Read on to see how you can do this. Back in the really old Azure ML days, you could download an Excel workbook which would have things set up and you could feed in a bunch of input data and get predictions.

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Model Deployment Options in Azure

Published 2022-05-30 by Kevin Feasel

Tori Tompkins enumerates ways to deploy machine learning models in Azure:

There are so many options to deploy models in Azure that is can get quite overwhelming. In this blog, we break down all the available options and consider the pros and cons of each tooling option.

Even with those, there are other approaches as well, like hosting Spark-based models in Azure Synapse Analytics, using SQL Server Machine Learning Services on an Azure SQL Managed Instance or VM running SQL Server, etc.

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