The Library · Insights
Deep dives across data, automation & AI
109 in-depth articles — from Excel foundations to production machine learning. Filter by topic or difficulty to find exactly what you need.
Power QueryPractitioner
Implementing Data Validation and Quality Checks in Power Query: Building Automated Assertion Pipelines to Catch Nulls, Duplicates, and Referential Integrity Failures Before Load
Learn how to build assertion-style validation pipelines in Power Query that automatically catch nulls, duplicate keys, and referential integrity failures before data reaches your model. This lesson gives you a complete, reusable quality gate architecture with copy-paste-ready M code for real production pipelines.
19 min read
Power QueryFoundation
Implementing Custom Environment and Credential Management with M Language let Scoping: Structuring Query Context for Multi-Source, Multi-Tenant Data Pipelines
Hard-coded connection strings scattered across dozens of queries are a deployment disaster waiting to happen. This lesson shows you how to use M's let scoping and record-based context objects to build multi-environment, multi-tenant Power Query pipelines where a single configuration change propagates correctly to every downstream query.
16 min read
Power QueryFoundation
Connecting to and Extracting Data from REST APIs with Pagination in Power Query: Handling Next-Page Tokens, Offset Parameters, and Rate Limits
Most REST APIs return data in pages, not all at once — and if you don't know how to follow the pagination, you're only seeing a fraction of your data. This lesson teaches you how to write M code that loops through every page of an API response, handles cursor tokens and offset parameters, and respects rate limits so your queries don't get blocked.
17 min read
Power QueryExpert
Implementing Custom Culture-Aware Text Classification and Regex Engine Pipelines in Power Query M: Building Reusable Pattern Libraries for Multilingual Data Standardization
Learn to build a full culture-aware text classification and standardization pipeline in Power Query M, complete with a versioned pattern library, a custom regex engine, pre-processing strategies, and normalizers for multilingual data — all packaged as reusable shared queries. This is the lesson that turns ad-hoc text cleanup into a systematic, production-ready system.
26 min read
Power QueryExpert
Implementing Multi-Tenant Data Isolation in Power Query: Parameterized Filtering, Credential Scoping, and Safe Query Patterns for Shared ETL Pipelines
Building a Power Query pipeline that serves multiple tenants from shared infrastructure requires more than adding a filter — it demands layered isolation guarantees, credential management discipline, and query patterns that fail safely when configuration goes wrong. This lesson teaches you the complete architecture for building multi-tenant ETL pipelines you can trust with sensitive data.
30 min read
Power QueryPractitioner
Implementing Web Scraping and Dynamic HTML Table Extraction Pipelines in Power Query M: Parsing Nested Structures, Handling Anti-Patterns, and Building Resilient Selectors
Go beyond Power Query's default web connector and build production-grade HTML scraping pipelines in M. This lesson covers nested table navigation, merged-cell handling, resilient column selectors, and JavaScript-rendered page extraction — with complete, copy-paste-ready code throughout.
22 min read
Power QueryPractitioner
Orchestrating Multi-Query Refresh Dependencies in Power Query: Controlling Load Order and Isolating Volatile Sources for Reliable Pipeline Execution
When Power Query evaluates a complex pipeline, it decides what to refresh, when, and how many times — unless you architect it otherwise. This lesson teaches you how to take control of evaluation order, buffer volatile sources like APIs and SharePoint lists, and build pipelines that are deterministic and reliable in production.
21 min read
Power QueryFoundation
Implementing Custom Number Formatting and Currency Conversion Pipelines in Power Query M: Locale-Aware Parsing, Rounding Strategies, and Multi-Currency Normalization
International financial data breaks standard Power Query type conversions in silent, dangerous ways. This lesson teaches you how to build locale-aware number parsers, implement currency-specific rounding rules, and normalize multi-currency transaction tables to a single base currency using a robust, production-ready M pipeline.
17 min read
Power QueryFoundation
Loading Data from SharePoint Lists and Libraries in Power Query: Connection, Authentication, and Practical Data Extraction
SharePoint is one of the most common data stores in the Microsoft ecosystem — and one of the trickiest to connect to cleanly. This lesson walks you through authenticating, constructing the right site URL, and extracting usable data from both SharePoint Lists and Document Libraries in Power Query.
19 min read
Power QueryExpert
Cross-Query State Management and Shared Parameter Tables in Power Query M: Centralizing Configuration for Multi-Report Deployments
Managing configuration across dozens of Power BI reports is a maintenance nightmare — unless you centralize it in M. This lesson walks you through building a production-grade shared parameter table system with environment switching, type-safe access, derived configuration, and health checking, all in native Power Query M.
26 min read
Power QueryExpert
Implementing Late-Arriving Facts and Out-of-Order Record Reconciliation in Power Query
Late-arriving records silently corrupt historical data in most Power Query pipelines — and most tutorials never mention they exist. This deep-dive lesson gives you a complete architectural framework for detecting, classifying, and reconciling backdated facts using watermarks, dual-date tracking, SCD2 temporal joins, and audit log patterns.
26 min read
Power QueryPractitioner
Custom Pivot and Unpivot in Power Query M: Dynamic Column Detection and Multi-Attribute Reshaping
The native Pivot and Unpivot buttons in Power Query break the moment your source schema changes. This lesson teaches you how to write M code that detects columns at runtime, handles multi-attribute unpivot, and builds custom aggregations that go far beyond what the UI can do. Walk away with production-ready patterns you can deploy immediately.
17 min read