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Category: Cloud

Microsoft Fabric Capacities and Reserved Instances

Published 2024-09-10 by Kevin Feasel

Marc Lelijveld shares an experience:

Last week, I had a situation in which a client wanted to purchase a reserved instance Fabric capacity. Me being me, I assumed it would be super straight forward to purchase through Azure. However, at some point I was lost in the process where the official documentation confused even more. In the end, I figured out and managed to get a capacity running based on the Reserved Instance pricing. I didn’t find any other blogs or articles describing this confusion or specific case. Therefore, I decided to write down my thoughts and findings in a blog.

This blog is not only relevant if you work with Microsoft Fabric, but also for anyone currently working with Power BI Premium. Given the deprecation of Power BI Premium capacities, you have to switch to Fabric capacities sooner or later.

Read on to learn more about the differences between pay-as-you-go and reserved instance capacities, the process to make a reservation, and what comes after that before you have a Microsoft Fabric capacity ready to go.

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Real-Time Streaming in Azure

Published 2024-09-06 by Kevin Feasel

Temidayo Omoniyi takes us through an architecture:

In today’s world, billions of data are generated daily from messaging applications like WhatsApp, financial data like the New York Stock Exchange, or video streaming platforms like YouTube. As a data engineer or solution architect, you are tasked to design a real-time streaming platform that captures the data as they are generated and stored in the necessary storage for decision-making.

This does a great job of going into detail, not only at the architectural level, but also setup and practical implementation.

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Exploring Azure Data Storage Options

Published 2024-09-06 by Kevin Feasel

Anurag K talks about data storage:

Choosing the right data storage option in Azure is critical for ensuring your applications run efficiently, securely, and cost-effectively. In this blog post, we’ll dive deeper into three key Azure storage services: Blob Storage, File Storage, and Disk Storage. We’ll explore their features, use cases, and provide specific examples to help you understand how to best leverage these services in your cloud environment.

I would note here that within the Blob storage section, you could carve out an explanation of Data Lake Storage Gen2, which starts from Blob storage and then adds hierarchical namespaces (i.e., folders).

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Running a Microsoft Fabric Notebook via Azure DevOps

Published 2024-09-05 by Kevin Feasel

Kevin Chant runs a notebook:

In this post I want to share one way that you can run a Microsoft Fabric notebook from Azure DevOps.

You can consider this post a follow-up to my last post about unit tests on Microsoft Fabric items. Since somebody asked me about automating notebooks and I wanted to show it in action.

Please note, currently the ability to call the API that runs a notebook on demand does not support service principals.

Despite that limitation, Kevin shows two ways to authenticate while calling the appropriate API.

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T-SQL Snapshot Point-in-Time Recovery to Azure VM

Published 2024-09-05 by Kevin Feasel

Anthony Nocentino continues a series on T-SQL snapshot backups:

In this post, the third in our series on using T-SQL Snapshot Backup, I will guide you through using the new T-SQL Snapshot Backup feature in SQL Server 2022 to take a snapshot backup and then perform point-in-time database restores using that snapshot backup as the base, but this time using an Azure Virtual Machine. We will explore how to manage Azure storage-level operations, such as taking snapshots, cloning snapshots, and executing an instantaneous point-in-time database restore from the snapshot with minimal impact on your infrastructure. Additionally, I will demonstrate a PowerShell script that utilizes dbatools and Azure Az modules to automate the process.

Read on for the script and plenty of details.

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Comparing Azure Event Hubs to Apache Kafka

Published 2024-09-03 by Kevin Feasel

Dharmbir Kashyap makes a comparison:

In the realm of event streaming and real-time data processing, choosing the right platform is critical to the success of your project. Two of the most popular options available today are Azure Event Hub and Apache Kafka. Both platforms offer robust solutions for handling large volumes of streaming data, but they are designed with different architectures, features, and use cases in mind. This blog post will delve into the key differences between Azure Event Hub and Kafka, helping you determine which platform is best suited for your specific needs.

Read on for an overview of each product and where each product fits.

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vCore-Based Subscription Limits for Azure SQL DB and Synapse Dedicated SQL Pools

Published 2024-08-27 by Kevin Feasel

Raj Tiwari announces a change in subscription limits:

New vCore based limits: The new limits will be based on vCores per Subscription per Region, which will be directly equivalent to DTU and DWU.

Default logical servers limit: The previous limits on Logical Server DTUs have been discontinued. All new and existing subscriptions will now have a default limit of 250 logical servers. 

Configurable vCore limits: Subscription vCore limits can now be easily managed through the support section on the Azure Portal, with approvals typically processed within minutes.

Read on to learn more about these limits and how you could extend them.

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Compressing Indexes and Shrinking Azure SQL MI Databases

Published 2024-08-27 by Kevin Feasel

Kendra Little has a good reason for an often-bad act:

Shrinking databases in SQL Server isn’t fun – it’s slow, it causes blocking if you forget to use the WAIT_AT_LOW_PRIORITY option, and sometimes it persistently fails and refuses to budge until you restart the instance. You only want to shrink a SQL Server database when you’ve got a good reason and a lot of patience.

If you’re using Azure SQL Managed Instance and you haven’t already used data compression on your indexes and shrunk your databases, you probably have two good reasons to do both of those things: performance and cost reduction.

Compressing indexes is very often (almost always?) a good thing. Shrinking databases is very often (again, almost always?) a bad thing. This is like a buddy cop movie for your database.

Kendra gives some good advice but also lays out a warning if you’re on General Purpose V1, so read the whole thing.

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