A Fine Slice Of SQL Server
Andy Brownsword creates a card:
Last week we looked at utilising Teams to make notifications more collaborative. This week I want to show how we can add more visual and interactive elements by adopting Adaptive Cards.
Andy shows the process for creating an adaptive card, as well as some of the gotchas you might run into along the way.
Leave a CommentMeagan Longoria takes a peek at mirroring in Microsoft Fabric:
Mirroring of SQL Server databases in Microsoft Fabric was first released in public preview in March 2024. Mirrored databases promise near-real-time replication without the need to manage and orchestrate pipelines, copy jobs, or notebooks. John Sterrett blogged about them last year here. But since that initial release, the mechanism under the hood has evolved significantly.
Read on to see how this behaves for versions of SQL Server prior to 2025, and how it changes in 2025.
Leave a CommentYuwei Xiao needs a way to move data:
When we released pg_ducklake, it brought a columnar lakehouse storage layer to PostgreSQL: DuckDB-powered analytical tables backed by Parquet, with metadata living in PostgreSQL’s own catalog. One question kept coming up: how do I keep these analytical tables in sync with my transactional tables automatically?
This is a real problem. If you manage DuckLake tables by hand, running periodic ETL jobs or batch inserts, you end up with stale data, extra scripts to maintain, and an operational surface area that grows with every table. For teams that want fresh analytical views of their OLTP data, this quickly becomes painful.
pg_duckpipe addresses this. It is a PostgreSQL extension (and optionally a standalone daemon) that streams changes from regular heap tables into DuckLake columnar tables in real time. No Kafka, no Debezium, no external orchestrator. Just PostgreSQL.
Click through to learn more about how it works.
Leave a CommentChris Webb covers a new feature:
I have been spending a lot of time recently investigating the new performance-related features that have rolled out in Fabric Dataflows over the last few months, so expect a lot of blog posts on this subject in the near future. Probably my favourite of these features is Preview-Only steps: they make such a big difference to my quality of life as a Dataflows developer.
The basic idea (which you can read about in the very detailed docs here) is that you can add steps to a query inside a Dataflow that are only executed when you are editing the query and looking at data in the preview pane; when the Dataflow is refreshed these steps are ignored. This means you can do things like add filters, remove columns or summarise data while you’re editing the Dataflow in order to make the performance of the editor faster or debug data problems. It’s all very straightforward and works well.
First up, that feature is pretty interesting, though I could see things break if you only do your testing in the preview pane. Second, what Chris does with this is quite interesting.
Leave a CommentKoen Verbeeck said something bad:
A while ago I blogged about a use case where a pipeline fails during debugging with a BadRequest error, even though it validates successfully. If you’re wondering, this is the helpful error message that you get:
Click through for an image of the 400 Bad Request message, how Koen fixed it originally, and then a different scenario in which that 400 message popped up.
Ultimately, a 400 Bad Request comes down to “You sent me information that doesn’t make sense and I can’t fulfill your request, so fix it, dummy.” 400 status codes are very rude and insulting. Especially 418–that thing has a mouth like a sailor’s.
Leave a CommentKoen Verbeeck glues together several technologies:
Quite the title, so let me set the stage first. You have an Azure Data Factory instance (or Azure Synapse Pipelines) and you have a couple of linked services that point to Fabric artifacts such as a lakehouse or a warehouse. You want to deploy your ADF instance with an Azure Devops build/release pipeline to another environment (e.g. acceptance or production) and this means the linked services need to change as well because in those environments the lakehouse or warehouse are in a different workspace (and also have different object Ids).
When you want to deploy ADF, you typically use the ARM template that ADF automatically creates when you publish (when your instance is linked with a git repo). More information about this setup can be found in the documentation. To parameterize certain properties of a linked service, you can use custom parameterization of the ARM template. Anyway, long story short, I tried to parameterize the properties of the Fabric linked service.
Read on to see how that went, as well as what you need to do to solve this issue.
Leave a CommentAndy Brownsword sends a message:
Whether running Data Factory, Synapse, or Fabric pipelines, things go wrong – and the de facto response is to send an email. We’ve looked at sending emails from pipelines before, but at scale they can become noise and are easy to ignore.
A more effective option is to surface alerts where collaboration already exists, such as Teams.
In this post we’re going to start looking at using Teams and consolidate notifications into a channel. This functionality gives team members visibility, the ability to update in threads, and the option to tag people for a tighter response loop than typical emails bring.
Click through for the process.
Leave a CommentMichael Bourgon writes a script:
I hate creating SSIS. I also hate creating ADF via GUI. So here’s a way to do it with python.
There are a couple prereqs (below), specifically an ultra-configurable Linked Service and Dataset.
Click through for an example of how it works.
Leave a CommentPenny Zhou sees recent datasets:
How much time do you spend navigating to the same data sources when building dataflows? Data preparation is an iterative process—you often return to the same sources as you refine your dataflows, add new transformations, or create similar workflows. If you find yourself repeatedly connecting to the same tables, files, or databases, the Recent data module in Dataflow Gen2 is designed for you. This feature reduces friction by providing quick access to your most frequently used data items, letting you focus on the transformation logic rather than navigation.
Click through to see how you can access the Recent data menu and what it includes.
Leave a CommentKenneth Omorodion builds a Dataflows Gen2 pipeline:
In a previous tip, I described how we can implement snapshot reporting using Microsoft Fabric Dataflow Gen2. In this article, I will describe how to achieve the same using Microsoft Fabric Pipelines. I previously described how important snapshot reporting can be in Business Intelligence reporting. Some reasons why developers/engineers might prefer to leverage a Fabric pipeline instead of a Dataflow Gen 2 include considerations around cost efficiency and data volumes.
My strong preference is still to do this in code (notebooks, Spark jobs), but at least Dataflows Gen2 aren’t literally 100x slower than the alternatives anymore.
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