🎯 Tab Selection Guide – What to Use When
Decision matrix for selecting the right tab for every data task
📊 Quick Decision Matrix
📦 Bulk Data
Best For: Importing/exporting large volumes
DATA INGESTION LOOKUPS EXPORT🐍 Python
Best For: Data analysis & transformation
ANALYSIS TRANSFORM VISUALIZE🧹 Data Cleaning
Best For: Dirty data from legacy systems
CLEANING VALIDATION MATCHING📊 Visualizations
Best For: Charts & dashboards
CHARTS DASHBOARDS REPORTS🤖 AI Assistant
Best For: Learning & troubleshooting
HELP TROUBLESHOOT GUIDANCE🔍 CRUD Operations
Best For: Single record operations
SINGLE RECORD TESTING QUICK FIXES🧠 Universal Query
Best For: Complex queries
COMPLEX QUERIES JOINS EXPLORATION🔍 I Want To… (Decision Guide)
📤 Import Data into Dataverse
Small CSV/JSON file (< 10,000 records)
Use: 📦 Bulk Data → Manual Ingestion
Quick and simple upload
Large dataset (> 100,000 records)
Use: 📦 Bulk Data → Manual Ingestion → Enable batch processing
Handles large volumes with progress tracking
Data with relationships (lookups)
Use: 📦 Bulk Data → Wizard Mode (Lookups)
Auto-resolves parent/child relationships
Dirty data needing cleaning
Use: 🧹 Data Cleaning → Clean → Export → 📦 Bulk Data
Two-step process: clean then import
From external API or database
Use: 🐍📝 Python Gen → Generate script → Run locally
Production-ready code for scheduled jobs
📥 Export Data from Dataverse
Simple table export
Use: 🧠 Universal Query Studio → Execute → Export
Direct OData query with export options
Filtered/selected records
Use: 📋 FetchXML/Query → Write query → Export
Full control with FetchXML
Multiple related tables
Use: 🧩 Multi-Table Query → Join tables → Export
Handles complex relationships
For analysis in Python
Use: 🐍 Python → query_dataverse() → pandas DataFrame
Direct to Python for analysis
🔄 Transform & Clean Data
Simple cleaning (email, phone, dates)
Use: 🧹 Data Cleaning → AI Column Matching → Apply rules
AI-powered auto-matching and cleaning
Complex transformations (business logic)
Use: 🐍 Python → pandas operations → transform data
Full Python flexibility for complex logic
Deduplication
Use: 🔄 Data Deduplication → Upload → Find duplicates
Specialized deduplication tools
Validate data quality
Use: 🧹 Data Cleaning → Validation rules → Check quality
Built-in validation for common data types
📊 Analyze & Visualize Data
Quick charts and graphs
Use: 📊 Visualizations → Select data → Choose chart type
Interactive charts without coding
Statistical analysis
Use: 🐍 Python → pandas + scipy → Statistical tests
Full statistical capabilities
Simple counts and aggregations
Use: 📊 Data Analysis → Select field → View counts
Quick field-level statistics
Advanced visualizations
Use: 🐍 Python → matplotlib/seaborn → Custom plots
Professional publication-quality charts
🔄 Common Workflow Patterns
Workflow 1: Data Migration
Source System → 🧹 Data Cleaning → 📦 Bulk Data (Wizard) → Dataverse
For: Legacy system migration with dirty data
Workflow 2: Analytics Pipeline
Dataverse → 🐍 Python (analysis) → 📊 Visualizations → Report
For: Monthly business reports, KPIs
Workflow 3: API Integration
External API → 🐍📝 Python Gen (script) → Schedule Task → Dataverse
For: Daily data sync from external systems
Workflow 4: Data Quality Audit
Dataverse → 🧹 Data Cleaning (validate) → Issues List → Fix
For: Quarterly data quality checks
⚠️ Common Mistakes & Corrections
❌ Mistake: Using Python for simple imports
Problem: Writing Python scripts when Bulk Data tab can do it in 2 clicks
✅ Correction: Use 📦 Bulk Data for imports > Use Python only for complex transformations
❌ Mistake: Manual lookups in Excel
Problem: Exporting to Excel, doing VLOOKUPs, re-importing
✅ Correction: Use 📦 Bulk Data → Wizard Mode for automatic lookup resolution
❌ Mistake: Complex queries in CRUD tab
Problem: Trying to write joins and aggregates in simple CRUD interface
✅ Correction: Use 🧠 Universal Query Studio or 🧩 Multi-Table Query
❌ Mistake: Direct imports without cleaning
Problem: Importing dirty data, then fixing errors one by one
✅ Correction: Always run through 🧹 Data Cleaning first
💡 Pro Tip: The 80/20 Rule
80% of tasks can be done with these 4 tabs:
- 📦 Bulk Data – For imports/exports
- 🧹 Data Cleaning – For data quality
- 🧠 Universal Query – For queries
- 🤖 AI Assistant – For help
Learn these well before exploring others!
📈 Performance Considerations
| Operation | Fastest Tab | Records/Second | When to Use |
|---|---|---|---|
| Simple Data Import | 📦 Bulk Data (Batch API) | 100-200/sec | Large volume imports |
| Import with Lookups | 📦 Bulk Data (Wizard) | 10-20/sec | Related data imports |
| Data Analysis | 🐍 Python (pandas) | Varies | Complex calculations |
| Query Execution | 🧠 Universal Query | 1000+/sec | Fast data retrieval |
| Data Cleaning | 🧹 Data Cleaning | 50-100/sec | Pre-import cleanup |
👥 By User Role
👨💼 Business Analyst
Essential Tabs:
- 📊 Visualizations – Create dashboards
- 📊 Data Analysis – Quick insights
- 📦 Bulk Data – Import data sources
- 🤖 AI Assistant – Get help
👩💻 Data Engineer
Essential Tabs:
- 🐍 Python – Complex transformations
- 📦 Bulk Data – ETL pipelines
- 🧹 Data Cleaning – Data quality
- 📋 Metadata – Documentation
👨🔧 System Administrator
Essential Tabs:
- 🌐 Environments – Manage connections
- 🗑️ Bulk Delete – Clean up data
- 📋 Metadata – Audit tracking
- 📜 History – Monitor API calls
🧑💻 Developer
Essential Tabs:
- 🧪 API Advanced – API testing
- 🐍📝 Python Gen – Code generation
- 🔍 CRUD Operations – Debug data
- 🤖 AI Support – Query generation
🔄 Integration Patterns
| Integration Type | Primary Tab | Secondary Tab | Output |
|---|---|---|---|
| Scheduled Data Sync | 🐍📝 Python Gen | 📦 Bulk Data (config) | .py script for Task Scheduler |
| Data Quality Dashboard | 🧹 Data Cleaning | 📊 Visualizations | Interactive quality dashboard |
| Compliance Documentation | 📋 Metadata Staging | 🐍 Python (export) | PDF documentation |
| Real-time Monitoring | 🐍 Python (continuous) | 📊 Visualizations (live) | Live monitoring dashboard |
🚀 Getting Started Recommendation
If you’re new to MDPM, follow this learning path:
- Week 1: Master 🌐 Environments and 📦 Bulk Data
- Week 2: Learn 🧹 Data Cleaning and 📊 Visualizations
- Week 3: Explore 🐍 Python for analysis
- Week 4: Try advanced features (Wizard Mode, Metadata)
Use 🤖 AI Assistant whenever you’re stuck!
❓ Still Not Sure? Ask the AI!
Don’t guess which tab to use! Go to 🤖 AI Assistant and describe your task:
- “I need to import customer data from a CSV with address lookups”
- “How do I create a monthly sales report?”
- “Best way to clean up duplicate customer records?”
- “I want to automate daily data imports from our ERP”
The AI will recommend the best tab and even give you step-by-step instructions!