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For years, Power BI Premium has been the go-to solution for organisations that wanted enterprise-scale analytics without leaving the Microsoft ecosystem. It offered dedicated capacity, large dataset sizes and predictable performance - all the ingredients needed for serious business intelligence. Then Microsoft Fabric arrived, and the landscape changed. Suddenly, analytics, data engineering, real-time streaming and machine learning all sat under a single umbrella. For many tea

Getting a machine learning model into production can feel like the finish line. In reality, it is just the start. The moment a model goes live, the world around it begins to shift. Customer behaviour changes, data pipelines evolve and assumptions that once held true slowly stop matching reality. Before long, performance slips and trust erodes. That quiet decay is model drift, and it catches almost every team out eventually. The hard part is knowing what to do about it. Do you

Many organisations are beginning to feel the limitations of their existing data platforms. Dashboards are delivering insights and data science teams are active, yet deploying machine learning models into production often remains slow, fragile and heavily reliant on handovers. Pipelines fail, model versions drift and teams frequently operate in silos. The problem is rarely a lack of ideas or talent. More often, the issue lies in the underlying platform. Databricks offers a pra

If you’ve worked with Power BI for a while, you eventually run into the same issue every developer does: dataset refreshes that take forever. When that happens, most people start tweaking DAX measures, but more often than not, the real slowdown happens before  DAX ever runs. The real story is how Power BI queries your data source. Three techniques usually make all the difference: query folding, SQL Profiler, and incremental refresh. Query Folding This is where performance tun