Business Professionals
Techno-Business Professionals
Power BI | Power Query | Advanced DAX | SQL - Query &
Programming
Microsoft Fabric | Power BI | Power Query | Advanced DAX |
SQL - Query & Programming
Microsoft Power Apps | Microsoft Power Automate
Power BI | Adv. DAX | SQL (Query & Programming) |
VBA | Python | Web Scrapping | API Integration
Power BI | Power Apps | Power Automate |
SQL (Query & Programming)
Power BI | Adv. DAX | Power Apps | Power Automate |
SQL (Query & Programming) | VBA | Python | Web Scrapping | API Integration
Power Apps | Power Automate | SQL | VBA | Python |
Web Scraping | RPA | API Integration
Technology Professionals
Power BI | DAX | SQL | ETL with SSIS | SSAS | VBA | Python
Power BI | SQL | Azure Data Lake | Synapse Analytics |
Data Factory | Databricks | Power Apps | Power Automate |
Azure Analysis Services
Microsoft Fabric | Power BI | SQL | Lakehouse |
Data Factory (Pipelines) | Dataflows Gen2 | KQL | Delta Tables | Power Apps | Power Automate
Power BI | Power Apps | Power Automate | SQL | VBA | Python | API Integration
Power BI | Advanced DAX | Databricks | SQL | Lakehouse Architecture
Business Professionals
Techno-Business Professionals
Power BI | Power Query | Advanced DAX | SQL - Query &
Programming
Microsoft Fabric | Power BI | Power Query | Advanced DAX |
SQL - Query & Programming
Microsoft Power Apps | Microsoft Power Automate
Power BI | Adv. DAX | SQL (Query & Programming) |
VBA | Web Scrapping | API Integration
Power BI | Power Apps | Power Automate |
SQL (Query & Programming)
Power BI | Adv. DAX | Power Apps | Power Automate |
SQL (Query & Programming) | VBA | Web Scrapping | API Integration
Power Apps | Power Automate | SQL | VBA |
Web Scraping | RPA | API Integration
Technology Professionals
Power BI | DAX | SQL | ETL with SSIS | SSAS | VBA
Power BI | SQL | Azure Data Lake | Synapse Analytics |
Data Factory | Azure Analysis Services
Microsoft Fabric | Power BI | SQL | Lakehouse |
Data Factory (Pipelines) | Dataflows Gen2 | KQL | Delta Tables
Power BI | Power Apps | Power Automate | SQL | VBA | API Integration
Power BI | Advanced DAX | Databricks | SQL | Lakehouse Architecture

Most Power Apps lessons aren’t learned during training.
They’re learned after deployment, when real users, real data, and real constraints show up.
After reviewing multiple live Power Apps implementations, the same lessons surface again and again — regardless of industry or app size.
When Power Apps struggle, it’s rarely because of the platform.
It’s usually because:
The app did exactly what it was designed to do.
Apps that worked perfectly in testing often struggled later.
Why?
Real data and real users expose assumptions very quickly.
Delegation issues weren’t “bugs” in most projects.
They were:
Teams that fixed delegation early avoided major rework later.
Apps became hard to change when:
Clear boundaries between UI logic, process logic, and data logic made the biggest difference.
Unexpected costs didn’t come from overuse.
They came from:
Licensing behaved exactly as designed — just not as expected.
The most stable apps weren’t the most sophisticated.
They were:
Complexity accumulated slowly — simplicity paid off quickly.
Successful Power Apps projects aren’t about using every feature.
They’re about:
Most problems were predictable in hindsight.
For those looking to understand how Power Apps behaves in real projects — beyond demos and tutorials, the Microsoft Power Apps Course by ExcelGoodies focuses on practical patterns drawn from live implementations, helping teams build apps that scale, perform, and remain maintainable.
Check the Upcoming batch details
Editor’s NoteThis article consolidates recurring patterns and lessons observed across live Power Apps implementations, typically identified during post-deployment reviews, enhancements, and redesign efforts.
Insights compiled with inputs from the ExcelGoodies Trainers & Power Users Community.
Power Apps
New
Next Batches Now Live
Power BI
SQL
Power Apps
Power Automate
Microsoft Fabrics
Azure Data Engineering