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Month: October 2024

Referencing a Microsoft Fabric ML Model from another Workspace

Published 2024-10-08 by Kevin Feasel

Sandeep Pawar crosses workspaces:

I have written a couple of blogs about working with ML models in Microsoft Fabric. Creating experiments and logging and scoring models in Fabric is very easy, thanks to the built-in MLflow integration. However, the Fabric Data Science experience has one limitation. There are no model endpoints yet, and you cannot load a model from another workspace because the model URI, unlike in Databricks, does not reference a workspace. If you use MLFlowTransformer as shown in this blog, only the model from the workspace where the notebook is hosted is loaded. However, there is a workaround.

Read on for that workaround, as well as the core limitation associated with it.

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Reduced Auto-Pause Delay for Azure SQL DB Serverless

Published 2024-10-08 by Kevin Feasel

Morgan Oslake goes to sleep sooner:

Azure SQL Database serverless automatically scales compute based on workload demand and bills for compute used per second.  In the General Purpose tier, serverless also provides an option to automatically pause the database during idle usage periods when only storage related costs are billed.  When workload activity returns, the database is automatically resumed.

Customers choosing to enable auto-pausing can specify the auto-pause delay as part of the serverless configuration.  The auto-pause delay is the length of time the database must be idle before auto-pausing.  The lower the auto-pause delay and the more frequently auto-pausing occurs, the greater the potential compute cost savings. 

Read on for the update in minimum auto-pause time.

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Fixing Missing SQL Agent Jobs Post-Migration

Published 2024-10-08 by Kevin Feasel

Lee Markum is looking for that lost shaker of SQL Agent jobs:

I’ve been doing migrations fairly continuously for the past 18 months. PowerShell has been my primary mechanism for many parts of the process, including copying jobs from the source SQL Server to the target. That has worked almost without incident each time. However, recently, an app team noticed that there were SQL Server Agent jobs missing on their new 2022 SQL Servers.  Because the first couple of missing jobs also existed on their Development environment, they were able to recreate those jobs in production. They naturally expressed concern that other jobs may be missing.

Read on for Lee’s process, including the solution.

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Announcements from the European Fabric Community Conference

Published 2024-10-08 by Kevin Feasel

James Serra brings tidings:

A TON of new features announcements at the European Microsoft Fabric Community Conference help last week. The full list is here, and I wanted to list my favorite announcements from that list:

  • Access Databricks Unity Catalog tables from Fabric (public preview): You can now access Databricks Unity Catalog tables directly from Fabric. In Fabric, you can now create a new data item called “Mirrored Azure Databricks Catalog”. When creating this item, you simply provide your Azure Databricks workspace URL and select the catalog you want to make available in Fabric. Rather than making a copy of the data, Fabric creates a shortcut for every table in the selected catalog. It also keeps the Fabric data item in sync. So, if a table is added or removed from UC, the change is automatically reflected in Fabric. Once your Azure Databricks Catalog item is created, it behaves the same as any other item in Fabric. Seamlessly access tables through the SQL endpoint, utilize Spark with Fabric notebooks and take full advantage of Direct La

Read on for the rest of what James found exciting.

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An Overview of k Nearest Neighbors

Published 2024-10-07 by Kevin Feasel

Harris Amjad explains a common algorithm for classification:

It so happens that given the hype of Machine Learning (ML) and especially Large Language Models these days, there is a considerable proportion of those who wish to understand how these systems work from scratch. Unfortunately, more often than not, the interest fades away quickly as learners jump to complicated algorithms like neural networks and transformers first, without giving heed to traditional ML algorithms that paved the foundation for these advanced algorithms in the first place. In this tip, we will introduce and implement the K-Nearest Neighbors model in Python. Although it is quite old, it remains very popular due to its simplicity and intuitiveness.

Click through to learn more about this algorithm, including an implementation from scratch in Python.

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Reading Data from Azure Blob Storage in Snowflake

Published 2024-10-07 by Kevin Feasel

Arun Sirpal explains a common architectural pattern:

Let’s go back to data platforms today and I want to talk about a very common integration I see nowadays, Azure Blob Storage linked to Snowflake via a storage integration which then we can access semi structured files via external tables, it is a good combination of technology I have to say.

Click through for an architecture diagram and example of the code you’d need.

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Constrained Kerberos Delegation with SSRS and Power BI Gateways

Published 2024-10-07 by Kevin Feasel

Rod Edwards doesn’t want just anyone to double-hop:

Ok, many of you will already be aware that in order to use Integrated Authentication successfully with SSRS particularly, that you have to configure Kerberos Authentication. At a very basic level, this allows the credentials of the user running the report, to be passed to the report server (hop 1) and then along to the target of the SSRS datasource (hop 2), also known as “Double hop” authentication. The delegation part of this signifies where the service (PBIG or SSRS) is allowed to pass these credentials along to.

  • anywhere, ie…Unconstrained delegation, or
  • to a restricted set of targets…Constrained delegation.

Read on to see how you can set up constrained delegation.

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Continuous Integration vs Continuous Delivery

Published 2024-10-07 by Kevin Feasel

Bravin Wasike disambiguates two terms:

Continuous Integration (CI) and Continuous Delivery (CD) are fundamental to DevOps and agile methodologies. They ensure that software is developed, tested, and delivered quickly and efficiently. CI and CD are more than just trending buzzwords. They are crucial processes that help teams deliver quality software at a high velocity. Understanding CI and CD is essential for anyone involved in software development or operations.

In this two-part series, we will demystify continuous integration vs. continuous delivery. In this part 1 article, we will discuss the basic concepts involved in Continuous Integration and Continuous Delivery. The article will highlight primary objectives of CI/CD, their benefits, CI/CD tools examples, key differences between CI and CD and real-world examples of CI/CD.

Read the whole thing. The way I quickly summarize continuous integration, continuous delivery, and continuous deployment is as follows:

  • Continuous integration: you check in code and the build process automatically runs tests to make sure all is well.
  • Continuous delivery: you check in code and, after the continuous integration process automatically runs tests, the continuous delivery process automatically builds the project and makes it available for push-button deployment.
  • Continuous deployment: you check in code and, after the continuous integration process automatically runs tests, the continuous deployment process automatically builds the project and deploys it. No human needs to be involved after check-in if all goes well.
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