Get skilled on Microsoft Fabric � the AI-powered analytics platform | Microsoft Fabric Blog | Microsoft Fabric

Power BI
Introducing git integration in Microsoft Fabric for seamless source control management | Microsoft Fabric Blog | Microsoft Fabric
New card visual | Public preview
Data obfuscation in Data Factory with Delphix Compliance Services - Microsoft Fabric
Fabric decision guide - copy activity, dataflow, or Spark - Microsoft Fabric
Lakehouse tutorial - Create your first lakehouse - Microsoft Fabric
Develop, execute, and manage notebooks - Microsoft Fabric
Data science in Microsoft Fabric - Microsoft Fabric
Lakehouse tutorial - Prepare and transform data in the lakehouse - Microsoft Fabric
Data warehouse tutorial - analyze data with a notebook - Microsoft Fabric
What�s New: May 2023: Dax Functions, and PBI Fabric � Ninmonkey
Power Query Templates In Excel And Fabric - Chris Webb's BI Blog
Re: How to filter data from Dax
Re: Histogram based on measure values
Calculating the trend of a Chart using DAX ( Smoothing Moving Average)
Importing Delta table from OneLake to PowerBI
Lakehouse VS. Warehouse VS. Datamart - The Difference Between The Three Fabric Objects
Introducing the end-to-end scenarios in Microsoft Fabric | Microsoft Fabric Blog | Microsoft Fabric
Fabric Notes - Simple drawings illustrating the main concepts of Microsoft Fabric
Introducing MATCHBY for DAX Window Functions
DAX Pareto Calculation - Phil Seamark on DAX
The Future of Business Intelligence Part 2: Dismantling the Supply Chain and Planting the Forest.
outgrows its roots simply falls over. Wave 3 of business intelligence is about a balanced approach to insight generation and distribution. It is not focused on needless growth and does not derive its value from the sheer amount of charts created, but rather its veracity and total value added.
Circulatory: If sap flows in only one direction the data tree dies. Wave 3 must support bi-directional interaction with decision makers and downstream systems to create feedback loops to drive growth and change. This must be built into the DNA of the tool.
So what the heck does this actually mean? The biggest set of changes I see coming for Wave 3 is the backswing of the ‘centralization - distribution’ technology pendulum into a place of balance, where the BI tool is a self-service insight generation platform that easily feeds into other important data processes, instead of being a black-box end point for the data supply chain.
To support this the platform must grow beyond just presenting dashboards. It needs to have an open, headless metrics store to feed AI/ML and apps
Data quality is going to matter even more than it does today, because of how compelling ChatGPT’s answers sound to humans. If your data sucks, it will very confidently give you sucky responses.
There is going to be a major ‘trough of disillusionment’ with this tech when it gets widely implemented in BI and 3% of its answers
A lot of firms may have very poor training data that results in very poor performance and a very bad initial impression.
Rooted: Just as a data tree grows best in great soil, Wave 3 requires an accurate foundation of clearly defined, valuable metrics that can feed any upstream process - whether that’s traditional BI, AI/ML or analytic/operational apps. These metrics are the foundation of balanced self-service.
On object | Public Preview (Opt-in) | Microsoft Power BI Blog | Microsoft Power BI
Data Quality: The Missing Link in Your Cloud Data Migration
What is Snowpark — and Why Does it Matter? A phData Perspective
Power BI adoption roadmap conclusion - Power BI
Everything correlates together: As you progress through each of the steps listed above, it's important that everything's correlated from the high-level strategic organizational objectives, all the way down to more detailed action items. That way, you'll know that you're working on the right things.
e
Icon Maker by Raycast
Power BI Ideas site improvement updates
PowerBi.tips tools
When can I use OFFSET DAX function in Power BI