🎨 Mapping Data Flows¶

Master visual data transformation using Mapping Data Flows for complex ETL logic without writing code.

📋 Table of Contents¶

• Data Flow Basics
• Transformation Types
• Next Steps

🎯 Data Flow Basics¶

Mapping Data Flows provide a code-free, visual way to design data transformations that execute on Spark clusters.

Create Data Flow¶

{
  "name": "SalesDataFlow",
  "properties": {
    "type": "MappingDataFlow",
    "typeProperties": {
      "sources": [
        {
          "name": "SalesSource",
          "dataset": {"referenceName": "SalesDataset"}
        }
      ],
      "sinks": [
        {
          "name": "CleanedSalesSink",
          "dataset": {"referenceName": "OutputDataset"}
        }
      ],
      "transformations": []
    }
  }
}

🔄 Transformation Types¶

Filter Transformation¶

Remove rows based on conditions.

Derived Column¶

Create calculated fields.

Aggregate¶

Group and summarize data.

Join¶

Combine multiple datasets.

📚 Additional Resources¶

• Mapping Data Flows
• Data Flow Performance

🚀 Next Steps¶

→ 12. Wrangling Data Flows

Module Progress: 11 of 18 complete

Tutorial Version: 1.0 Last Updated: January 2025