The Library · Insights
Deep dives across data, automation & AI
982 in-depth articles — from Excel foundations to production machine learning. Filter by topic or difficulty to find exactly what you need.
Power AppsFoundation
Connecting Canvas Apps to REST APIs Using Custom Connectors: A Step-by-Step Guide for Beginners
Learn how to build a Custom Connector in Power Apps from scratch and use it to pull live data from any REST API into your Canvas App. This hands-on guide walks through every step using a real weather API, so you leave with a working app and a skill you can apply to any external data source.
15 min read
Power BIFoundation
Designing a Star Schema Data Model in Power BI Desktop for Enterprise Reporting
Learn how to build a star schema data model in Power BI Desktop from scratch using a realistic retail scenario. This hands-on lesson covers fact tables, dimension tables, relationships, and validation — giving you the modeling foundation every enterprise Power BI report depends on.
16 min read
Power BIFoundation
DAX Text and String Functions: Cleaning, Parsing, and Categorizing Data with SEARCH, SUBSTITUTE, and FORMAT
Real-world data is messy — inconsistent phone numbers, embedded category codes, date values stored as text. This lesson teaches you how to use DAX's SEARCH, SUBSTITUTE, and FORMAT functions to clean, parse, and categorize string data, with practical examples you can apply immediately in Power BI.
14 min read
Power BIFoundation
Introduction to DAX: Writing Your First Calculated Columns and Measures in Power BI
DAX is the formula language that transforms Power BI from a chart tool into a genuine analytical engine. In this hands-on lesson, you'll learn the difference between calculated columns and measures, write your first DAX formulas from scratch, and understand the powerful CALCULATE function — all using a realistic sales dataset.
15 min read
Power QueryFoundation
Working with Dates, Times, and Duration Values in Power Query M
Date and time calculations break more Power Query projects than almost any other topic. This complete lesson teaches you how M's temporal type system works, how to parse text into proper date types, and how to build real time intelligence patterns from scratch — with hands-on exercises and common error fixes included.
15 min read
Power QueryFoundation
Grouping and Aggregating Data in Power Query: Summarize, Count, and Calculate with Group By
Raw transaction data is only useful when you can summarize it. This lesson teaches you how to use Power Query's Group By feature to collapse thousands of rows into meaningful summaries — by region, by product, by sales rep, or any combination you need. Walk away with real, practical aggregation skills you can apply immediately.
16 min read
Career DevelopmentExpert
Designing and Selling a Fractional Head of Data Offer: How to Transition from Project Work to a High-Retainer Leadership Role
Most experienced data freelancers are selling their time when they should be selling their judgment. This deep-dive lesson shows you exactly how to design, price, and close a fractional Head of Data offer — moving from unpredictable project revenue to stable, high-value retainers with clients who treat you as a strategic leader.
31 min read
Data EngineeringExpert
Incremental Models at Scale: Strategies for Efficiently Processing Late-Arriving Data and Partition Pruning in dbt
Most dbt incremental models work fine in development and silently destroy performance at production scale. This deep-dive lesson teaches you how partition pruning actually works in BigQuery, Snowflake, and Databricks, how to design configurable lookback windows for late-arriving data, and which incremental strategy — merge, insert_overwrite, or append — to use and when.
26 min read
Data EngineeringExpert
Schema Evolution Strategies for Production Data Pipelines: Handling Breaking Changes Without Downtime
Schema changes are the silent killer of production data pipelines. This deep-dive lesson covers the full spectrum of schema evolution strategies — from Schema Registry compatibility modes and the expand-contract SQL pattern to multi-version pipeline architecture and Iceberg's field-ID system — so you can ship changes without taking anything offline.
26 min read
AI & Machine LearningExpert
Graph RAG: Building Knowledge Graph-Enhanced Retrieval Pipelines for Complex Multi-Hop Queries
Standard RAG fails when answers require tracing relationships across multiple pieces of information. Graph RAG combines vector search with knowledge graph traversal to handle exactly these multi-hop queries—and this lesson shows you how to build the full pipeline from scratch, including ingestion, entity extraction, graph construction, and hybrid retrieval.
24 min read
AI & Machine LearningExpert
Agentic Loop Design: Building Reliable Multi-Step LLM Pipelines with Retry Logic, State Management, and Human-in-the-Loop Escalation
Most LLM agent tutorials show you the happy path. This lesson shows you the full picture — how to design agentic loops that survive tool failures, persist state across crashes, classify errors intelligently, and pause gracefully for human review without losing progress. By the end, you'll have the engineering foundations for agents you'd actually trust in production.
26 min read
AI & Machine LearningExpert
Retrieval-Augmented Generation in Practice: Building Knowledge-Grounded AI Pipelines for Enterprise Data Workflows
RAG sounds simple until you deploy it in production. This deep-dive lesson covers every layer of a real RAG pipeline — from chunking strategy and hybrid retrieval to cross-encoder reranking and RAGAS evaluation — with production-grade Python code you can actually use.
28 min read