The Library · Insights
Deep dives across data, automation & AI
1261 in-depth articles — from Excel foundations to production machine learning. Filter by topic or difficulty to find exactly what you need.
Microsoft FabricPractitioner
Implementing a Fabric Notebook-Based Data Quality Framework: Validating Row Counts, Null Thresholds, and Referential Integrity Across Medallion Layers Before Pipeline Promotion
Bad data that passes silently through your medallion pipeline is worse than a pipeline that fails loudly. This lesson walks you through building a reusable PySpark quality framework in Microsoft Fabric that validates row counts, null rates, and referential integrity at each layer — and wires the results into your pipeline so failures actually stop promotion.
22 min read
Power AutomateExpert
Automating FTP and SFTP File Transfers in Power Automate Desktop: Connecting to Remote Servers, Uploading Batch Files, and Handling Transfer Errors in Unattended RPA Workflows
FTP and SFTP automation in Power Automate Desktop goes far beyond drag-and-drop upload actions — production-grade workflows require protocol awareness, secure credential management, per-file verification, and layered error handling. This lesson teaches you to build unattended transfer automation that handles real-world failures reliably, including the PowerShell/WinSCP path for SFTP that PAD's native actions don't cover.
29 min read
Microsoft FabricExpert
Unpivoting, Aggregating, and Reshaping Lakehouse Data in Dataflow Gen2: Advanced Power Query Transformations Before Writing to Delta Tables
Learn how to use advanced Power Query M techniques in Dataflow Gen2 to unpivot wide source data, aggregate with Table.Group, and reshape tables into clean, typed Delta table schemas in your Microsoft Fabric lakehouse. This lesson covers the full M query structure, type-fixing patterns, and the honest trade-offs between Dataflow Gen2 and Spark for complex transformations.
27 min read
PythonExpert
Building a Self-Updating Sales Dashboard in pandas and openpyxl: Automated Charts, Conditional Formatting, and Named Ranges from a Live Data Source
Learn how to build a professional Excel sales dashboard that regenerates itself from a live data source using pandas and openpyxl. Covers automated chart generation, four types of conditional formatting, named ranges, and Excel Tables — all from a clean, schedulable Python script.
24 min read
Power AppsExpert
Configuring Dataverse Managed Properties and Solution Component Locking: Controlling Customizability, Preventing Downstream Modifications, and Enforcing ISV-Grade Solution Boundaries in Model-Driven Apps
Managed properties are Dataverse's mechanism for declaring exactly what downstream consumers can modify after your managed solution is installed — and most ISVs configure them wrong. This expert-level lesson covers the full component taxonomy, XML-level configuration, solution layering interactions, and a practical boundary strategy for building platform-grade solutions.
28 min read
Microsoft FabricExpert
Joining Multiple Delta Tables Across Fabric Lakehouses and Warehouses in a Single Spark Notebook: Cross-Workspace Queries, OneLake Paths, and Writing Results to a Gold Layer Table
Learn how to read Delta tables from multiple Fabric workspaces — across lakehouses and warehouses — in a single PySpark notebook. This expert lesson covers OneLake ABFS path construction, programmatic GUID resolution, multi-table joins with broadcast optimization, and writing results to a gold-layer Delta table using overwrite and MERGE patterns.
24 min read
Power AutomateExpert
Implementing Dynamic Selector Repair and Fallback Strategies in Power Automate Desktop: Detecting Broken UI Elements at Runtime and Switching to Alternative Identification Methods Without Flow Failure
UI changes break RPA bots — but they don't have to break your flows. This expert-level lesson teaches you how to build cascading fallback chains in Power Automate Desktop that detect broken selectors at runtime, attempt dynamic repair, and gracefully degrade through image recognition and OCR before ever raising a fatal error.
25 min read
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