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Power BI does not have immediate direct connection capabilities to Jupyter notebooks. Yet, there are alternatives for integration. You can output your data from Jupyter notebooks (with Python) to other places like SQL databases, Azure Blob Storage or even CSV files in OneDrive or Sharepoint. Power BI can be connected to these data sources with automatic refresh rates or real-time data refresh rates (configuration, gateway, etc.). Although not technically “real-time”, you do have a pipeline from outputting your computations in a notebook to having next-level dashboards in Power BI.
For even more advanced operations, Jupyter notebooks hosted in environments such as Azure Machine Learning can expose their outputs as web services (APIs). Power BI can consume these APIs using Power Query and a custom connector collection to get dynamic insights, again not in real-time but based on service response time and dataset refresh configurations.
For professional developers wanting to access data science capabilities via integrated modules in Power BI, there are Power BI Course in Pune that include a module on how to connect to Python-based environments. Along with that, there are Power BI Training in Pune that go into detail to help students understand operating via scripting and automation so they can connect other platforms like Jupyter to Power BI as part of their business analytic framework.