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Data Transformation

Data processing, transformation, and manipula## Need Help?

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Overview​

The Data Transformation category provides essential tools for processing and manipulating data within your workflows. These nodes enable you to clean, convert, validate, and restructure data to meet your specific requirements.

Subcategories​

Data Parsing​

Parse and extract data from various formats including JSON, XML, CSV, and custom text formats.

Format Conversion​

Convert between different data formats and structures with automatic type handling.

Data Aggregation​

Aggregate and summarize data using statistical operations, grouping, and calculations.

Data Validation​

Validate data integrity, format compliance, and business rule adherence.

Key Features​

  • Format Support: Wide range of data formats (JSON, XML, CSV, YAML, etc.)
  • Type Safety: Automatic type detection and conversion
  • Schema Validation: Validate data against predefined schemas
  • Performance Optimized: Efficient processing for large datasets
  • Error Handling: Comprehensive error reporting and recovery
  • Custom Transformations: Support for custom transformation logic

Common Use Cases​

  • Data Cleaning: Remove duplicates, normalize formats, handle missing values
  • ETL Processes: Extract, transform, and load data between systems
  • API Data Processing: Transform API responses to required formats
  • File Processing: Parse and process uploaded files
  • Data Migration: Convert data during system migrations
  • Report Generation: Aggregate data for reports and analytics

Data Formats Supported​

  • JSON: JavaScript Object Notation
  • XML: Extensible Markup Language
  • CSV: Comma-Separated Values
  • YAML: YAML Ain't Markup Language
  • TSV: Tab-Separated Values
  • Excel: Microsoft Excel files
  • Parquet: Columnar storage format
  • Custom: User-defined formats

Best Practices​

  • Data Validation: Always validate data before transformation
  • Error Handling: Implement robust error handling for malformed data
  • Performance: Consider memory usage for large datasets
  • Schema Design: Use well-defined schemas for consistent results
  • Testing: Test transformations with representative data samples
  • Documentation: Document transformation logic and requirements

Getting Started​

  1. Identify your data sources and target formats
  2. Choose the appropriate transformation nodes
  3. Configure data mapping and validation rules
  4. Test with sample data
  5. Implement error handling and monitoring

Need Help?​