A Fine Slice Of SQL Server
Ancy Philip makes an announcement:
We’re excited to announce that the long-awaited refresh SQL analytics endpoint metadata REST API is now available in preview. You can now programmatically trigger a refresh of your SQL analytics endpoint to keep tables in sync with any changes made in the parent artifact, ensuring that you can keep your data up to date as needed.
Click through to see how it works.
Comments closedTeo Lachev is waiting for a message:
A current project called for mirroring a Google BigQuery dataset to Fabric. This feature is currently in private preview so don’t try to find it. However, the tips I share here should be applicable to other available mirroring scenarios, such as mirroring from Azure SQL Database.
One of the GBQ tables was a transaction fact table with some 130 million rows. The issue was that the mirroring window would show this table as normally replicating table with Running green status, but we waited and waited and nothing was happening…
Read on to learn more and how Teo was able to get a better idea of how the initial sync progressed.
Comments closedSunitha Muthukrishna takes us through a debugging exercise:
Debugging your code is important to identify issues and mitigate them when you’re working with user data functions in Microsoft Fabric. You want to make sure everything works as it should and that’s where local debugging lets you catch problems in your code without messing with the live environment. In this blog post, I will walk you through the steps to make local debugging easier and faster.
Click through to see what you’ll need, as well as the process to debug a function locally.
Comments closedRob Sewell continues a series on working with Microsoft Fabric assets via Powershell:
Having created a workspace, a lakehouse, a warehouse, a Fabric SQL database previously, it’s time to move onto something a little more interesting. I was getting bored writing the same post over and over again, so I thought I would show you how to list the items in a Fabric Workspace using PowerShell. This command was super useful for me today to answer the question “What do they have in this workspace?” and I hope it will be useful for you too.
Click through to see how you can accomplish this.
Comments closedYael Biss doesn’t want people walking off with the data:
As data volume and complexity soar, protecting sensitive information has become non-negotiable. With the latest enhancements to Purview Data Loss Prevention (DLP) Policies in Microsoft Fabric, organizations now have the power to proactively secure their data in Onelake.
Whether you’re just getting started or looking to take your data governance to the next level, following proven best practices will maximize your security, compliance, and productivity.
Click through for several tips on how to use Microsoft Purview DLP in Fabric. One of those tips ought to be “Get a side hustle so you can afford both Purview and Fabric.”
Comments closedWith this update, Azure Mirrored Databricks Catalog items can now be enabled with OneLake security. Security at the table, column, or row level can be defined directly in each item, allowing access to be controlled at a granular level. This allows security to be defined directly over the data mirrored into OneLake so it can be securely used by downstream sources such as lakehouses, notebooks, or semantic models.
Read on to see what to do in order to make use of this. It is, of course, currently in preview.
Comments closedRob Sewell has a trio of posts. First up, Rob creates a warehouse:
We are going to use [FabricTools, a PowerShell module that simplifies the management of Microsoft Fabric resources. You can find it on GitHub at[FabricTools provides functions to create, update, and delete Fabric warehouses and other resources. It has been developed and is maintained by members of the community.
Having created a Microsoft Fabric workspace using PowerShell, assigned users to it using PowerShell, and created a Microsoft Fabric Warehouse using PowerShell, we now turn our attention to creating a Microsoft Fabric Lakehouse using PowerShell. These posts all look remarkably similar 🙂 and thats because the functionality they are showing is exactly same..
Just like dbatools and dbachecks creating functions that follow the same pattern makes it easier to learn. This is why FabricTools has chosen to use the same pattern for creating Fabric resources using PowerShell.
And from there is a Fabric SQL Database:
A Fabric SQL Database is a SQL Database that is hosted in Microsoft Fabric. It provides a fully managed SQL database service that allows you to store and query your data using SQL using the same SLQ Engine as Azure SQL Database.
Click through for examples of all three.
Comments closedMarc Lelijveld explains some terms:
It was only yesterday, that I wrote a blog post on Semantic Models as a source for Fabric Data Agents. Not much time has passed, since I learned that Fabric Data Agents does not (always) respect the Synonyms that have been added to a Semantic Model. As a result, the Data Agent may start creating implicit measures, not respecting the definitions and logic in the explicit measures that are part of the Semantic Model.
Long story short, I think we should be able to do better! Therefore, I created a Notebook that helps you to setup Data Agents, collect additional information from your Semantic Model and populate that information automatically as AI notes to the Data Agent.
Read on for the notebook and some additional explanation.
Comments closedMeagan Longoria spots the fork in the road:
At the moment there are two activities in Fabric pipelines that allow you to execute a “child” pipeline. They are both named “Invoke Pipeline” but are differentiated by the labels “Legacy” and “Preview” in parentheses.
Read on to learn more about these two and why choosing the new one may not always be the best option for you, at least not yet.
Comments closedGilbert Quevauvilliers continues a series on Python notebooks and DuckDB:
In this blog post I am going to explain how to loop through a data frame to query data and write once to a Lakehouse table.
The example I will use is to loop through a list of dates which I get from my date table, then query an API, append to an existing data frame and finally write once to a Lakehouse table.
Click through for the code, as well as a sample notebook you can use.
Comments closed