Azure Data Engineer Training Online | at Visualpath
                     Mapping Data Flows in 
Azure Data Factory
Understanding visual data transformations in ADF, enabling 
code-free ETL/ELT at scale, and exploring this powerful Azure 
data engineering feature.
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Introduction to Mapping Data Flows
What Are They?
Mapping Data Flows are visual, no-code data 
transformation features built directly into Azure Data 
Factory. They empower data engineers to build scalable, 
production-grade pipelines without writing complex code.
Under the hood, these flows run on Azure Databricks 
using managed Spark clusters, providing enterprise-
scale compute power with automatic optimization.
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Why Mapping Data Flows?
No Code Required GUI-Based Design
Eliminates the need for complex scripts and manual Drag-and-drop transformation canvas makes complex 
Spark programming. Build transformations visually on data operations accessible to all engineers, reducing 
an intuitive canvas. development time.
Auto-Scaling Compute Enterprise Ready
Automatically scales compute resources for large Purpose-built for production ETL/ELT workloads with 
datasets, optimizing performance and cost without reliability, monitoring, and integration across Azure 
manual cluster management. services.
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Key Capabilities
1
Rich Transformations
Perform joins, aggregates, pivots, filters, lookups, and derived columns through visual 
components. Handle complex business logic without code.
2
Streaming & Batch
Supports both batch processing and real-time streaming scenarios. Build unified pipelines that 
handle diverse data velocity requirements.
3
Debug with Data Preview
Interactive debugging with live data previews at each transformation step. Validate logic before 
deployment with sample data inspection.
4
Dynamic Parameterization
Build parameterized, reusable flows that adapt to different sources, schemas, and business rules dynamically at runtime.
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How Mapping Data Flows Work
Data Extraction
Data is extracted from various sources via linked services (Azure SQL, ADLS, Cosmos DB, etc.) using secure connections.
Spark Transformation
Transformations execute on managed Spark clusters in Azure, automatically optimized for your 
workload size and complexity.
Data Loading
Transformed outputs are loaded to data lakes, warehouses, or databases in your desired format and partition strategy.
Pipeline Orchestration
Flows are orchestrated within ADF pipelines with triggers, dependencies, error handling, and monitoring capabilities.
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Types of Data Flows
Mapping Data Flows
Visual batch transformations with full Spark capabilities. Ideal for 
complex ETL logic, production workloads, and enterprise-scale 
processing.
• Preferred for production environments
• Full transformation library
• Spark-native execution
Wrangling Data Flows
Power Query-based data preparation. Lightweight option for simpler 
cleansing and shaping tasks with familiar Excel-like interface.
• Quick data prep scenarios
• Power Query integration
• Lightweight processing
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Common Use Cases
Data Cleaning Complex Joins SCD Type 1 & 2
Remove duplicates, handle nulls, Execute multi-way joins, lookups, Implement Slowly Changing 
standardize formats, and validate and data enrichment across Dimensions to track historical 
data quality rules across millions of disparate sources with optimized changes in data warehouses with 
records. Spark join strategies. built-in merge patterns.
Aggregation & Enrichment Schema Drift Handling
Calculate metrics, summarize data, Automatically adapt to changing 
and enrich records with derived source schemas without breaking 
columns and business logic pipelines, enabling resilient data 
transformations. ingestion.
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Integration with Azure 
Services
Data Sources & Sinks Security Integration
Works seamlessly with Supports Azure Key 
ADLS Gen2, Azure SQL Vault for secure 
Database, Synapse credential management 
Analytics, Cosmos DB, and managed identities 
and 90+ connectors. for authentication.
Pipeline Orchestration
Runs inside ADF pipelines with triggers, control flows, 
error handling, and dependency management.
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Benefits for Data Engineers
5x 100+ Auto
Faster Transformations Spark Scaling
Development
Built-in operations Automatic compute 
Reduce time-to- eliminate custom optimization without 
production with code for common cluster 
visual design and patterns management 
reusable overhead
components
Key advantages: Build scalable Spark-based transformations 
without coding, create reusable flow components across projects, 
and orchestrate end-to-end data pipelines entirely within Azure 
Data Factory for unified monitoring and governance.
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For More Information About 
Azure data engineering
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Thank You
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