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Category: Cloud

New Features in Azure SQL MI Instance Pools

Published 2024-02-01 by Kevin Feasel

Djordje Marinkovic shows off what’s new:

When migrating small SQL Server instances to Azure it is often the case that a single SQL Managed Instance turns out to be overkill in terms of size and, consequently, cost. The oversizing problem can happen whenever very small instances are required, for example when an ISV company builds a multi-tenant app requiring a small SQL MI instance for each customer. In such cases the smallest size (4-vCores) for a single SQL MI can still turn out to be too large and too expensive for the given use case. This is where SQL MI pools (“instance pools”) deliver great value.

Click through for more information on instance pools, as well as new features for instance pools.

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Working with Erik Darling’s Stored Procedures in Azure SQL DB

Published 2024-01-31 by Kevin Feasel

Josephine Bush tries out some stored procedures:

Erik Darling, founder of Darling Data, has created these fantastic stored procedures to query SQL Server more efficiently to get health, log, or performance information. I will go through them here regarding using them in Azure SQL database since I don’t have any SQL Servers I manage anymore.

Read on to see which ones you can use in Azure SQL DB and which require SQL Server.

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Linked Servers from SQL MI using Azure Entra ID

Published 2024-01-31 by Kevin Feasel

Luis Aranda has the first of a two-part series:

Lately, we have seen some customers interested on the options available to use linked servers from Managed Instance and using Entra Authentication (formerly Azure Active Directory). It is certainly possible to create Linked Servers on SQL Managed instance (SQL MI) to connect to other PaaS databases such as other SQL MIs, Azure SQL Databases or Synapse databases using Entra Authentication.

Click through to see how you can do this using a managed identity. In the next article, Luis promises to show us how to do it with pass-through authentication, so you use your credentials instead of the managed identity’s credentials to access the remote server.

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Running Spark Jobs on Databricks with Spark Connect and .NET

Published 2024-01-24 by Kevin Feasel

Ed Elliott runs a Databricks job:

This post aims to show how we can create a .NET application, deploy it to Databricks, and then run a Databricks job that calls our .NET code, which uses Spark Connect to run a Spark job on the Databricks job cluster to write some data out to Azure storage.

In the previous post, I showed how to use the Range command to create a Spark DataFrame and then save it locally as a parquet file. In this post, we will use the Sql command, which will return a DataFrame or, in our world, a Relation. We will then pass that relation to a WriteOperation command, which will write the results of the Sql out to Azure storage.

The code is available HERE

Read on for the description of how everything works.

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Architecting a Public-Facing Azure Container Registry

Published 2024-01-24 by Kevin Feasel

Kumar Ashwin Hubert and Rajesh Singh share an architecture with us:

This reference architecture describes the deployment of secured Azure Container Registry for consuming docker images and artifacts by customer applications over external (public internet) network.

This architecture builds on Microsoft’s recommended security best practices to expose private applications for external access. It utilizes the ACR’s token and scope map feature to provide granular access control to ACR’s repositories. Also, ACR internally uses the Docker APIs, and it is recommended to be familiar with these concepts before deploying this architecture.

I think this is a great example of the good and the bad of Azure architectures. The good is that you get a thoughful, well-explained, thorough description of the services you need and how they fit together, and there are a lot of those in the Azure Architecture Center. The bad is that, if I want to secure one container registry, I need a dozen different services. If we didn’t have this particular architecture diagram, I doubt 1 in 50 cloud specialists would come up with all of these services.

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The Tradeoffs of Azure SQL Managed Instance General Purpose

Published 2024-01-23 by Kevin Feasel

Kendra Little provides some guidance:

Whether or not you use Azure SQL Managed Instance, you will likely be asked for an opinion on it eventually if you’re a SQL Server person.

While the architecture is documented, it can be a bit of a long read and some of the gotchas are spread out over different pages – so I’m drawing up the architecture of each service tier along with notable implications for the design on performance and cost. Here’s the scoop on General Purpose.

Click through for Kendra’s thoughts.

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Tips for Saving Money in the Cloud

Published 2024-01-22 by Kevin Feasel

David Klee offers up some advice:

You might be able to shave off thousands – or more – in your monthly cloud bills for your critical SQL Servers, all while maintaining or even improving performance.

Public cloud providers charge organizations for everything they deploy, and while a few items in the cloud are based on a pay-by-consumption model, most of the services that really stack up, namely SQL Server licensing, are charged on a pay-by-allocation model. If you provision a SQL Server VM with eight cores and promptly forget about it, you will be shocked at the end of the month when you receive your cloud subscription bill. If you provision a 32-core VM, and your workload only uses four cores, you are still paying for all 32 cores, regardless of the low utilization rates. The same goes for memory and storage. A large memory footprint and lots of high-speed attached managed disks all add up.

Read on for some advice around figuring out the right server size for your workload.

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Exporting to CSV in Azure ML Designer

Published 2024-01-19 by Kevin Feasel

Tom LaRock saves a file:

The most popular feature in any application is an easy-to-find button saying “Export to CSV.” If this button is not visibly available, a simple right-click of your mouse should present such an option. You really should not be forced to spend any additional time on this Earth looking for a way to export your data to a CSV file.

Well, in Azure ML Studio, exporting to a CSV file should be simple, but is not, unless you already know what you are doing and where to look. I was reminded of this recently, and decided to write a quick post in case a person new to ML Studio was wondering how to export data to a CSV file.

Click through for one false start and then the correct answer.

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