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Category: Cloud

Working with Blob Leases

Published 2026-08-07 by Kevin Feasel

Miles Cole explains how leasing works with Blob Storage and Data Lake Storage blobs:

Leased a blob? No, this isn’t some new car model with a great leasing offer. Blobs are files in ADLS / OneLake, and leasing is the process of temporarily holding an exclusive lock on one of those files. Does this sound useful in data engineering? Maybe not at first, but in this post I’ll show why understanding the extended features of object storage APIs like blob leases can be invaluable when building data systems that scale.

I’ve never actually done this, but it’s a smart way of preventing multiple job agents from picking up the same work.

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Setting up Azure SQL DB in Podman

Published 2026-08-03 by Kevin Feasel

Vlad Drumea builds a local version of Azure SQL Database:

Microsoft has recently released in private preview (at the time of writing) the Azure SQL Database container image.
This is Microsoft’s solution for folks who want to build and test Azure SQL DB native software without having to incur additional Azure costs just for dev/test/demo work.

This provides a local option for developers who want to work on products that require Azure SQL DB’s specific developer surface: T-SQL, drivers, and engine behavior.

Click through to see how this works. As of right now, you’ll need to be in the private preview, but I imagine things won’t change significantly once they move to public preview.

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Test and Validate Azure SQL Database Migrations

Published 2026-07-27 by Kevin Feasel

Marlon Ribunal has a tool:

Introducing azsql-migration-test, a small open-source CLI that validates your Azure SQL Database migrations against a local Azure SQL Database Developer container — the same engine as the cloud, running on your machine.

The problem: proving a migration works shouldn’t require the cloud

The tool is AI-generated and it looks like the blog post is as well, but it does look to be useful.

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Azure SQL Developer

Published 2026-07-27 by Kevin Feasel

Carlos Robles and Vandana Mahtani make an announcement:

Big news: Azure SQL Developer is here, in private preview. It’s the Azure SQL Database engine, on your laptop, in a container. Build against the exact engine you run in the cloud. Ship the same code to Azure. Change one line, the connection string, and you’re in production. Free for local dev and CI. No subscription. No credit card. No catch. Run it yourself, or hand it to an AI agent and watch it go. 

That’s pretty neat, especially because there is a surface area difference between Azure SQL Database and SQL Server.

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Looping Databases on an Azure SQL Instance

Published 2026-07-27 by Kevin Feasel

Jess Pomfret has a script:

In the on-prem world, or when we’re working with SQL Servers on VMs, wherever they might live it was pretty easy with dbatools to run a query against all the databases, collect some info and collate it into one record set. This used to work with Azure SQL Instances too – but Azure auth, or cloud auth in general is hard and recently the Connect-DbaInstance command needed to change to make it more reliable.

So, this is a quick post to cover how we can still manage this.

Click through to see how.

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Saving Unity Catalog Tables in Microsoft OneLake

Published 2026-07-01 by Kevin Feasel

Gerhard Brueckl pushes boundaries:

Microsoft and Databricks recently announced the next step of their collaboration and integration. It is now possible to store Databricks Unity Catalog tables directly in Microsoft OneLake. Here are the official announcement from Microsoft: https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/Extending-interoperability-Azure-Databricks-can-now-store-Unity/ba-p/5199741

Both parties have been working together to make this possible: Microsoft introduced the new item type Azure Databricks Storage and Databricks added support for OneLake for Unity Catalog External Locations (which can then be used to store the actual data). The UC External Location would then simply point to the storage endpoint provided by the Azure Databricks Storage item in Microsoft Fabric.

Click through to see what Gerhard found, as well as the results of some experimentation.

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Using Entra ID Authentication in Azure Database for PostgreSQL

Published 2026-06-19 by Kevin Feasel

Taiob Ali wants to use Entra for authentication:

During the livestream of my session at the POSETTE: An Event for Postgres 2026 conference, I received the following question on the hallway track via Discord:

I have one question : I added an Entra group as PostgreSQL Entra administrator, created/mapped the PostgreSQL role for that group, and granted permissions to that group. I am a member of the Entra group, but I still cannot log in with my own Entra user. Does Azure PostgreSQL allow group members to authenticate through the group role, or must each user also be individually created/mapped as a PostgreSQL role?

Read on for Taiob’s full answer.

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Automating Azure SQL DB Tasks without SQL Agent

Published 2026-06-17 by Kevin Feasel

Garry Bargsley solves a problem:

Many routine administrative tasks that have traditionally been handled by SQL Agent still need to be performed:

  • Scheduled stored procedures
  • ETL processes
  • Report generation
  • Data cleanup
  • Monitoring and alerting
  • Business process automation

However, Azure SQL Database does not include SQL Agent.

Garry provides several solutions, and I would add to it third-party job scheduling solutions. Granted, that’s usually an extra expense (whether due to fees or supporting a roll-your-own solution), but it’s on the table. And some of them are better than what SQL Agent has to offer, even if I do like the fact that there’s an okay option built-in for DBAs.

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Re-Migration and Data Engineering

Published 2026-06-11 by Kevin Feasel

Andy Leonard shares some thoughts:

Right-sizing didn’t always work out the way some clients were led to believe it would.

In nearly every instance, the right-sizing argument was presented (sold) as the solution to over-provisioning, or purchasing hardware to serve peak loads. The classic example was a US income tax service that needed more and faster compute available to meet increasing demand starting in late January and peaking in mid-April each calendar year. After mid-April, hardware that was beefy enough to handle that peak load sat mostly idle for the next 9 months.

I don’t think I’ve ever worked for a company where this scenario really made sense. Even in the e-commerce company where a sizable fraction of our total annual revenues happened over a 5-day period, the load was still significant enough the rest of the year that we made good use of our on-premises SQL Server hardware.

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