109 in-depth articles — from Excel foundations to production machine learning. Filter by topic or difficulty to find exactly what you need.

Undocumented Power Query pipelines become maintenance nightmares fast. Learn how to use step naming conventions, inline M comments, query descriptions, and metadata records to build queries that explain themselves — to colleagues, auditors, and future you.

Learn how to replace brittle hardcoded `if-then-else` chains with proper lookup tables and substitution pipelines in Power Query M. This lesson covers Record-based lookups, Table joins, null handling, and reusable function patterns for professional data standardization.

Duplicate records silently corrupt your reports and inflate your metrics. This hands-on lesson teaches you every deduplication technique in Power Query — from one-click removal to precise, sort-controlled survivor selection — so your data is clean and your analysis is trustworthy.

Building a global data model means reconciling timestamps from Salesforce, SAP, e-commerce platforms, and logistics partners — all storing time in different formats and zones. This expert-level lesson walks you through building a complete, production-grade M pipeline for UTC conversion, DST boundary detection, and cross-region calendar normalization — including the southern hemisphere edge cases and performance patterns that trip up experienced developers.
Full-load refreshes that process millions of rows to capture hundreds of changes are wasteful and slow. Learn how to implement production-grade watermark-based Change Data Capture in Power Query — complete with persistent high-water mark storage, query-folded delta fetches, multi-table orchestration, and recovery logic for real-world edge cases.

Power Query M has no native window functions — no OVER, no PARTITION BY, no ROWS BETWEEN. This lesson teaches you to build rolling averages, cumulative totals, partitioned ranks, and lag/lead functions from scratch using M's list operations and grouping patterns, packaged into a reusable function library you can use across any project.

Learn how to build a reusable M code framework that automatically profiles every column in your Power Query pipeline — computing completeness rates, statistical distributions, IQR and Z-score outlier flags, and pass/fail quality gates on every refresh. Stop discovering data problems after the model is built.

Learn how to flatten self-referencing parent-child tables in Power Query M using recursive functions. Build full ancestor breadcrumb paths, dynamic depth levels, and individual level columns for any org chart or category tree — no DAX or SQL required.

M — Power Query's formula language — represents all data using three container types: Tables, Lists, and Records. Learn how each one works, how to navigate inside them with M syntax, and how they nest together to form the complex structures you see in JSON and API data.

Most Power Query guides stop at CSV and Excel. This lesson goes deeper — teaching you to parse raw binary files at the byte level, including fixed-width mainframe exports, custom delimited byte streams, and proprietary formats with dynamic headers, all inside native M code.

Surrogate key generation is one of the most failure-prone steps in data warehouse ETL — especially when sources disagree on key formats. This deep-dive lesson teaches you deterministic hashing, offset-aware sequence indexing, and a robust cross-source mapping table pattern in pure M code.
When your source system has no CDC support, no reliable timestamps, and millions of rows, you need to build incremental logic yourself. This lesson walks through watermark tracking, hash-based row diff detection, and three-way table synchronization — all in pure M, all production-ready.