ETL and data preparation in Power BI.
7 tutorials in this category
Group By in Power Query lets you summarize rows by category — count, sum, average, min, max — using a visual interface. Learn the UI, advanced grouping with multiple columns, the M code behind it, and the mistakes that produce wrong totals.
Conditional columns let you create new columns based on if-then-else logic using a visual interface — no M code required. This guide covers the UI, nested conditions, the M code underneath, and the patterns for categorizing, flagging, and bucketing data.
Power Query has hundreds of functions, but a small set covers the vast majority of real-world cleanup tasks. This guide walks through ten transformations that handle messy exports, inconsistent naming, and malformed data — the daily reality of every data analyst.
Parameters let you control Power Query behavior without editing the M code — swap environments, change date ranges, or toggle between production and test data with a dropdown. This guide covers creating parameters, using them in queries, and the pattern for environment switching.
Merge and append are the two ways to combine tables in Power Query, and they do completely different things. Merge is a SQL-style join — it adds columns. Append is a union — it stacks rows. This guide covers the difference, the join types, and the performance implications of each.
M is the functional language behind Power Query. It looks nothing like DAX or Excel formulas, but once you understand its three core data structures and how functions compose, the editor becomes far less intimidating. This guide covers the fundamentals with practical examples.
Power Query is the ETL engine behind Power BI. Learn to connect, transform, and shape data with a no-code interface plus a peek at the M language underneath.