3 Things: Data Analytics Highlights from October 2023
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By Michael A
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This is a new blog series where we will summarise news relating to Power BI, Microsoft Fabric, and the Azure Data Platform, listing three significant things for each from the previous month.

The aim here is to provide you with a 'skimmable' list of news and announcements that we think you should know about and that you may have missed due to the influx of data analytics news each month. We will briefly summarise the articles in one or two lines so you can quickly decide which ones are relevant to you and worth reading.

So, without further adieu, here is this month's '3 Things':

Power BI


  • Deployment pipelines in Power BI are now customisable, so you can now pick the names of the stages, their order and how many stages. Learn more.

  • The 'Model Explorer' in Power BI brings the ability for you to create calculations groups in the Power BI desktop and more efficiently manage the model. Learn more.

  • Power BI Desktop OneDrive and SharePoint integration improvements make it easier to save Power BI files to OneDrive and share them through it. Learn more.



Microsoft Fabric


  • Data factory now has the ability for you to comment out part of a pipeline, making it more straightforward to troubleshoot pipelines or unit test an isolated part without resorting to pipeline duplication. Learn more.

  • Introduction of Semantic link in Microsoft Fabric, a new capability that enables your data scientists to effortlessly consume Power BI semantic models with a few lines of Python. Learn more.

  • Data Activator is now in public preview, which brings with it the ability for you to automate monitor, detect, and act automatically on signals from your data. Learn more.



Azure Data Platform


  • Delta Lake 3.0 was released, and it brings you Delta UniForm, a new feature that allows your Delta tables to be read from compute engines or applications that can only read Apache Hudi and Apache Iceberg tables. Learn more.

  • The Databricks Unity Catalog has a new capability that uses AI to generate documentation for you, thus streamlining your data cataloguing efforts. Learn more.

  • The MLOps capabilities within Databricks Lakehouse AI were extended to bring you cross-workspace governance, end-to-end lineage, and state-of-the-art models. Learn more.



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