A Fine Slice Of SQL Server
Josephine Bush doesn’t want to do things one at a time:
I was sick of changing context in Azure Data Studio while cleaning up things discovered in our vulnerability assessments.
Read on to see how you can do this, but also see the complexity involved.
Comments closedBen Johnston looks at the art of the possible:
In my last post I described issues that might stop your migration to a SQL Server Managed Instance (SQL MI). This covers configuration items that differ or are not supported in SQL MI. These likely won’t stop your migration, but they could slow you down if you aren’t ready for these changes.
As with previous issues discussed, testing your migration is key. Validate all of your settings and be prepared to make some changes during your migration process. Most of the incompatible options make sense when you think about the purpose of SQL MI – it is controlled by Microsoft. Hardware settings, local file access, high-availability settings, and auditing are configured differently or completely disabled.
Click through to see what you can and cannot do when it comes to configuration.
Comments closedIn this video, I cover some news from Microsoft around the deprecation of the Azure Machine Learning SDK v1. We’ll take a look at the upgrade guide and see what it will take to perform this upgrade.
Microsoft will still support the SDK v1 until September of 2026, so we have a year to get code sorted out. The CLI v1, however, will go away sooner, so be sure you’re keeping up on that.
Comments closedKoen Verbeeck leaves the confines of Microsoft Fabric:
In the blog post Call a Fabric REST API from Azure Data Factory I explained how you can call a Fabric REST API endpoint from Azure Data Factory (or Synapse if you will). Let’s go a step further and execute a Fabric Data Pipeline from an ADF pipeline, which is a common request. A Fabric capacity cannot auto-resume, so you typically have an ADF pipeline that starts the Fabric capacity. After the capacity is started, you want to kick-off your ETL pipelines in Fabric and now you can do this from ADF as well.
Click through for the process. Though do check the warnings that Koen offers around either spending extra money by remaining in synchronous execution mode, or always getting a positive result in asynchronous execution mode, regardless of whether the underlying Fabric Data Pipeline worked or not.
Comments closedJosephine Bush schedules a task:
pg_cron is a simple cron-based job scheduler for PostgreSQL that runs inside the database as an extension. It allows you to schedule PostgreSQL commands directly from your database, similar to using cron jobs at the operating system level. pg_cron on PG Flex is pretty easy to use, making it easy to schedule regular database maintenance and processing tasks directly from within PostgreSQL.
Read on to see how to install the extension, and then how to manage cron jobs. Josephine also lays out some limitations when using pg_cron on Azure and how to track failed jobs.
Comments closedEvanjalin Joseph lays out a comparison:
Take ShopSmart, a global retail chain that operates both online and offline. The company wants to combine its sales, inventory, and customer data in order to facilitate real-time reporting and predictive analytics. Two top platforms are being assessed by the IT team for this change.
Azure, Power BI, and Microsoft 365 are already widely used by ShopSmart, which is in line with Fabric’s integrated ecosystem. The alternative, however, provides more multi-cloud flexibility and strong performance on structured data. The group has to choose between selecting a more specialized warehousing solution with more deployment options or making use of its current Microsoft investments.
Let’s examine the differences between the two platforms.
Click through for an overview of each platform and how they stack up against one another.
Comments closedJared Westover doesn’t want to share:
Users want to pull data from tables in an Azure SQL database into Excel via Power Query. This situation sounds simple. However, I don’t want to provide direct access to the database for several reasons, including the potential governance and permissions nightmare. We have a Fabric workspace, and most of the data already exists in Power BI reports. How can we give users access to the data they need without providing direct access to the database for an easy SQL export to Excel?
Click through for the answer. This solution is a bit more roundabout than granting direct database access, but also comes with a host of security benefits.
Comments closedKoen Verbeeck runs into a problem:
I recently had a new pipeline fail. It was actually a copy of an old pipeline where I had made some adjustments into as part of a database migration. When triggered during an execution run, it failed saying some expression could not be parsed. When I went into the pipeline and triggered a debug, it immediately failed with the following helpful error message:
Click through for the error message and how Koen was able to fix the issue.
Comments closedSuppose you want to call a certain Microsoft Fabric REST API endpoint from Azure Data Factory (or Synapse Pipelines). This can be done using a Web Activity, and most Fabric APIs now support service principals or managed identities. Let’s illustrate with an example. I’m going to call the REST API endpoint to create a new lakehouse.
Click through for the instructions.
Comments closedBen Johnston has an after-action report:
I was recently on a project to migrate a very transactional installation of SQL Server to Azure SQL Managed Instance (MI). SQL Managed Instance is a good stepping stone between a full, on-prem SQL instance / Azure VM and an Azure SQL Database. It has most of the functionality of a full, on-prem instance, with management of the SQL engine, backups, OS and underlying hardware done by Microsoft. It allows you to use cross database queries and run SQL Agent jobs, with fewer limitations than Azure SQL Database migrations.
The migration process isn’t completely seamless. During the migration of this system, we encountered several surprises. Hopefully, this will help you avoid, or at least be prepared for these differences from the on-prem version. This also reinforces the importance of testing each aspect of your migration.
This is part one of a two-parter and focuses on issues during the deployment process. Ben promises a follow-up with post-deployment issues you could run into. I expect that’s where the “What is this performance?” issues will come into play.
Comments closed