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Expert roadmaps, tutorials, salary guides and interview prep across Data Science, Generative AI, Python, Cloud & Analytics — written by GloryTecks trainers in Hyderabad.
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Power BI Certification (PL-300): How to Prepare
How to prepare for Microsoft's PL-300 Power BI Data Analyst exam, from the official study guide: the four skill areas and their weights, a study plan weighted to match, newer topics such as Copilot, Direct Lake and visual calculations, the passing score, the free practice assessment and sandbox, and annual renewal.
Star Schema vs Snowflake Schema in Power BI
Star versus snowflake schemas for Power BI models: what a snowflake dimension is, why Microsoft generally favours a single flattened table, a real five-table product dimension flattened into one with every key kept, what flattening quietly drops, and the situations where keeping separate tables makes sense.
Power BI Custom Visuals: When and How to Use Them
Using custom visuals in Power BI: the three places visuals come from, what Microsoft's certification does and does not check, the data, licensing and update risks of third-party code, features a non-certified visual loses, how organisations approve visuals centrally, and a checklist before adding one to a report.
Power BI Paginated Reports vs Interactive Reports
Paginated and interactive Power BI reports compared: what each is built with, how they get data, why only paginated reports print every row of a long table, parameters versus cross-filtering, export formats and subscriptions, licensing, which jobs suit each, building a first paginated report, and the limitations.
Power BI Themes and Branding for Professional Reports
How to brand Power BI reports with themes: how the base theme, custom theme and visual formatting layer, why theme colors beat custom colors, series color order, the theme JSON file with colors, text classes, visual styles and presets, and a contrast check that finds weak colors before readers do.
Aggregations in Power BI for Large Datasets
Power BI aggregations for large datasets: how an in-memory aggregation table answers summary queries over a huge DirectQuery fact table, why the grouping grain decides whether it helps, shown on real data where one grain is 345 times smaller and another barely smaller, which queries hit, and automatic aggregations.