The Library · Insights
Deep dives across data, automation & AI
978 in-depth articles — from Excel foundations to production machine learning. Filter by topic or difficulty to find exactly what you need.
Microsoft FabricPractitioner
Implementing Table Partitioning in a Fabric Lakehouse: Choosing Partition Keys, Writing Partitioned Delta Tables with PySpark, and Pruning Partitions for Faster SQL and Spark Queries
Unpartitioned Delta tables make Spark read everything before filtering anything. This lesson teaches you how to choose partition keys that actually improve performance, write partitioned gold-layer tables with PySpark, and verify that partition pruning is firing in both Spark and the SQL analytics endpoint.
22 min read
Power AppsExpert
Configuring Dataverse Table Capacity and Storage Partitioning: Managing Large-Table Performance, Elastic Tables, and Time-Series Data Strategies in Model-Driven Apps
When your Dataverse tables grow to tens of millions of rows, standard optimization tricks stop working. This lesson teaches you the architectural decisions — elastic tables, partition key strategies, TTL, and time-series patterns — that determine whether your model-driven app scales or buckles under production load.
31 min read
Power AutomateExpert
Automating Multi-Step Web Scraping and Pagination Handling in Power Automate Desktop
Most web scraping tutorials cover the happy path. This lesson covers production reality: dynamic tables, pagination loops that detect the last page automatically, session timeouts mid-run, and writing thousands of extracted rows to clean CSV and Excel output files — without losing a single row when something goes wrong.
27 min read
Microsoft FabricExpert
Implementing End-to-End Pipeline Error Handling in Microsoft Fabric: If Condition Activities, Failed Dependencies, and Email Alerts
Build production-grade error handling in Microsoft Fabric pipelines that actually catches failures, routes them intelligently, and sends actionable email alerts — across Copy, Dataflow Gen2, and Notebook activities. Learn the dependency condition model, activity output expressions, and centralized error handler patterns that keep your medallion pipelines resilient.
26 min read
PythonExpert
Automating pandas Data Cleaning with Custom Validation Rules, Error Logs, and a Corrected Output File
Learn how to build a production-grade data cleaning pipeline in pandas that validates rows against custom business rules, accumulates a structured error log, applies traceable corrections, and exports a clean output file alongside a stakeholder-ready validation report. This is the system that replaces three hours of manual Excel work with a single script.
26 min read
Power AppsPractitioner
Configuring Dataverse Environment Variables in Model-Driven App Solutions: Managing Connection References, Default Values, and Deployment-Time Overrides Across Environments
Environment variables are the key to building Power Platform solutions that deploy cleanly across dev, UAT, and production without manual edits. This deep-dive lesson covers the full lifecycle: creating typed variables, managing the default/current value split, binding connection references, and injecting overrides at deployment time using Power Platform CLI and deployment settings files.
23 min read
Power AutomateExpert
Automating Mainframe Terminal Sessions in Power Automate Desktop: Connecting via TN3270 and TN5250 Emulators, Navigating Green Screen Menus, and Extracting Structured Data for Modern System Integration
Mainframes still run the world's most critical business data, but extracting it means navigating green screen terminals most modern tools can't touch. This expert-level lesson teaches you how to connect Power Automate Desktop to TN3270 and TN5250 emulators, navigate menu hierarchies reliably, and parse fixed-width screen data into structured tables for modern system integration.
31 min read
Microsoft FabricPractitioner
Deduplicating and Cleansing Lakehouse Delta Tables with PySpark: Drop Duplicates, Fill Nulls, and Enforce Data Quality Rules Across Medallion Layers
Dirty data in bronze silently corrupts silver and gold. Learn how to build a production-grade PySpark cleansing pipeline that removes exact and soft duplicates, handles nulls with intentional strategies, quarantines bad rows, and enforces data quality rules with exceptions — all structured around the medallion architecture in Microsoft Fabric.
20 min read
Power AppsPractitioner
Configuring Dataverse Connection Roles and Relationship Categories: Modeling Party-to-Party Associations Between Records in Model-Driven Apps
Standard lookups can't capture the full complexity of who plays what role in a relationship — a contact might be a sponsor on one project and a stakeholder on another. This lesson teaches you how to design, configure, and deploy Dataverse connection roles for real-world professional scenarios, including reciprocal pairs, table restrictions, form surfaces, and security.
24 min read
PythonFoundation
Calculating Business Metrics in pandas: Revenue, Margin, Conversion Rate, and Other KPIs from Raw Transaction Data
Learn how to turn raw transaction data into real business KPIs using pandas. This hands-on lesson walks through computing revenue, gross margin, conversion rate, average order value, and customer-level metrics — with the common pitfalls explained so you get the numbers right.
13 min read
Power AutomateExpert
Automating Report Generation and Distribution in Power Automate Desktop: Exporting Data from Windows Applications, Merging into Excel Templates, and Delivering Files via Email Without Human Intervention
Learn how to build a production-ready, end-to-end report automation pipeline in Power Automate Desktop — from extracting raw data out of Windows applications to merging it into formatted Excel templates and delivering the finished report via email, completely unattended. This expert-level lesson covers architecture, error recovery, credential security, and the edge cases that break real-world flows.
28 min read
Microsoft FabricExpert
Implementing Incremental Refresh for Direct Lake Semantic Models in Microsoft Fabric: Configuring Delta Table Partitioning, Framing Policies, and Triggering Refresh via the XMLA Endpoint
Direct Lake incremental refresh isn't a checkbox — it requires aligning Delta table partitioning, TMDL framing policies, and XMLA-triggered refresh operations into a precise orchestration chain. This lesson gives you the full architecture, the code, and the troubleshooting knowledge to implement it correctly in production.
26 min read