Category: Microsoft Fabric
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Introduction In the world of Microsoft Fabric and Power BI, performance is directly tied to efficiency. One of the single greatest contributors to slow reports, sluggish refresh times, and unexpected capacity overloads is the unnecessarily large data model. While the VertiPaq engine (the technology behind Power BI’s storage) is famously efficient at compression, bloat still…
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Designing an effective semantic model in Power BI often starts with a star schema — a simple, proven structure that keeps your data model clear, fast, and scalable.However, even small design missteps can lead to slower performance, confusing results, or hard-to-maintain models. Below are the most common star schema mistakes we see in real-world implementations…
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When building semantic models, one of the most important decisions is choosing a star schema. This simple yet powerful structure centers around a fact table, surrounded by descriptive dimension tables. Widely adopted, the star schema improves clarity, boosts performance, and easily scales for large datasets . What are Dimension Tables? Dimension tables add context to…
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Incremental refresh is one of the most effective ways to improve performance and reduce capacity usage in Microsoft Fabric and Power BI. Instead of reloading all your data with every refresh, incremental refresh updates only the recent data that has changed. This means: But, and this is important, incremental refresh must be configured correctly. If…
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When Microsoft Fabric capacities hit their limits, users feel it instantly: reports slow down, dashboards lag, and refreshes fail. The problem? Most self-service BI users have no idea what’s really happening. Instead, they: In reality, it could even be their own work causing the slowdown — heavy DAX queries, repeated ad-hoc refreshes, or incremental refresh…
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Today we’d like to introduce one of the tools from our portfolio – Fabric Monitor. In organizations that have implemented Self-Service BI, a common issue is ungoverned solutions created directly by business users. Very often these solutions are sub-optimal in areas such as data processing and refreshes, model architecture, report design, or DAX measures. The result?…
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This is the 7th part of our Fabric Capacity Management series. If you haven’t yet, check out the earlier articles: As we explained in the previous article, Microsoft Fabric is a capacity-driven model. This means you purchase a certain amount of resources and pay for them — whether you use them fully or not. Ideally,…
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This is the 6th part of our Fabric Capacity Management series. If you haven’t yet, check out the earlier articles: Testing on production is one of the oldest IT jokes – everyone knows it’s risky, yet it still happens. In Fabric, the problem is especially visible in self-service scenarios. Think about it: a single shared…
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This is the 5th part of our Fabric Capacity Management series. If you haven’t yet, check out the earlier articles: There is no single way companies manage their Fabric capacities. Sometimes, such management is basically non-existent. There is no dedicated person or team monitoring and maintaining the capacity. Users are left to themselves. No one…
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This is the 4th part of our Fabric Capacity Management series. If you haven’t yet, check out the earlier articles: Monitoring tools are essential for effective management of Microsoft Fabric capacities. The native solution provided by Microsoft — Fabric Capacity Metrics — offers insight into the most important metrics, such as: However, this built-in report…