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PowerBI

Set up Power BI incremental refresh in minutes, without admin approval, production access or a single line of code. 👀

So, we’ve been tinkering, tankering and tunkering, and we have our latest release. 🚀 Power BI Incremental Refresh is a free, open source tool built for anyone who has ever babysat a slow refresh.

💻 Work locally, no sign in needed. Open a Power BI Project (.pbip) straight from your computer and set up incremental refresh in a few clicks. Your files never leave your browser. Download the updated model, test it in Desktop and commit it to git.

🔧 Or work directly in the Power BI service. Pick a published model, answer a few questions, and it adds the parameters, filters and refresh policy for you. No Tabular Editor, no XMLA scripts. You see every change line by line before it’s applied, with a backup and one click undo.

🩺 Health check a whole workspace. It scans every semantic model and flags: • Tables quietly loading only one month of data because a policy was lost on republish • Policies that duplicate rows • Filters that can’t fold to the source, so every refresh still reads everything • Failed and slow refreshes creeping towards the time limits • Big fact tables that should be using incremental refresh but aren’t

Every problem comes with a one click fix, and you can download the results as a report for your team.

🔒 Security reviewed, with the results in the README. There’s no backend and no data stored, it acts with your own Power BI permissions, and nothing is loaded from third party sites.

👉 Try it now: https://pbi-incremental-17fe26.gitlab.io/ Open your own .pbip project, or click “Demo the health check” to see it find problems in seconds.

I’d love to hear from anyone working with Power BI or Fabric. What’s the most painful refresh you’ve had to babysit? 👇

End-to-end product & global market analysis for Abstract — built on Databricks/PySpark + SQL Server + Power BI. SIAM1838 IFTS Group 6.

Analisi della transizione energetica nell'UE27 (2000–2030) con Python, Power BI e Machine Learning. Tre indici sintetici originali (ITE, ICP, RGI) calcolati su dati Our World in Data e proiettati al 2030 tramite regressione lineare.

Progetto IFTS Data Analysis & AI - SIAM1838