An instructional guide to using Power Query Editor in Power BI to transform, clean, and optimize data before it becomes part of reports. Covers UI components, transformation workflow, M script basics, query folding, staging, performance tips, and best practices for reliable data models.
Power Query Editor Fundamentals — the part of Power BI where your data gets a personality (and a haircut) "You don't clean data because it's fun. You clean it because your dashboards deserve dignity." — probably a data hero You're coming fresh from "Connecting to Data ...
What is Power Query Editor and why it matters (fast elevator pitch) Power Query Editor is the visual environment inside Power BI Desktop where you transform, clean, and shape data before it becomes part of your reports. Think of it as the backstage crew for your dashboard's Broadway debut: tr...
The UI — the parts you’ll cozy up to Queries pane (left): Each source or transformation is a query. Data preview (center): Live snapshot of the current step. Applied Steps (right, in Query Settings): The step-by-step log of transformations. Formula bar: Shows the M code for the current step...
The transformation workflow (the practical ritual) Connect (you did this — good) Inspect: Scan column headers, types, nulls Transform: Rename, change types, split columns, remove rows Shape: Pivot/unpivot, merge queries, group, aggregate Optimize: Enable query folding, disable load for in...
Key concepts, demystified (and meme-ready) Applied Steps Each action you take becomes a step. Steps are sequential and deterministic, which means: move one, and the rest keeps time like a polite clock. You can reorder some steps, but not always safely. Changing a type-conversion step before a...
Query Folding — the performance fairy Query Folding means Power Query is smart enough to translate your transformations back to the source (e.g., SQL) so the heavy work happens in the database, not locally. Why care? Less data over the wire, faster refreshes, happier users. How to check? In P...
Practical transformations you'll use every other day Change Data Types: Always do this early. Bad types = bad aggregations. Split Columns: Delimiters, fixed width, or by positions. Merge Queries (Left/Right/Inner/Full): SQL-style joins for enriching tables. Group By: Summaries and aggre...
Advanced-ish tips for real-world reliability Use Parameters for server names, file paths, and date ranges — makes switching environments (dev → prod) easy. Be careful with locale and encoding for CSVs. Avoid heavy custom M if it prevents folding; prefer native SQL or source views if performan...
Closing: Your power moves as a Power Query user Always get types right early. Prefer Reference over Duplicate for derived tables. Keep an eye on Query Folding and push work to the source when possible (this complements your previous work on connectivity and incremental loads). Use Disable L...
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