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Martini Data Models

Data models in Martini are structured blueprints for your application data. They define the properties and their types, such as String, Integer, Boolean, or Date, and can include nested models to represent complex hierarchies. Models can also reference other models to create inheritance relationships and reduce duplication.

Serving as reusable templates, data models describe your data’s structure, enforce validation, and ensure consistency across services, workflows, APIs, and database operations. They work seamlessly with structured data formats such as JSON, XML, and YAML, making data handling predictable and type-safe, whether you are building REST APIs, processing files, or transforming data between systems.

Topics Covered

  1. Creating/Deleting: Learn how to create data models from scratch using the wizard, generate them automatically from JSON/XML/YAML sources, and safely delete models while checking for dependencies.
  2. Import/Export: Transfer data model files between your file system and Martini packages through simple drag-and-drop imports and one-click download exports for backup, sharing, and collaboration.
  3. Data Model Editor: Master the visual editor interface for building and organizing model properties, including toolbar functions, property configuration, drag-and-drop operations, and preview formats for JSON, XML, and YAML output.
  4. Property Types: Explore all 15 available property types (String, Integer, Model, etc.) and their comprehensive meta-properties for validation, serialization, array handling, date formatting, and documentation.
  5. Object Converters: Understand how Martini automatically converts between different data types during service/workflow execution, plus learn to create custom converters for specialized business logic and domain-specific transformations.
  6. Model References: Implement model inheritance and reuse by referencing existing models as properties, enabling modular design patterns while maintaining consistency and reducing maintenance overhead across packages.
  7. Cursors: Process millions of records efficiently using memory-optimized cursors that stream data without loading entire datasets into memory, essential for large file processing and database operations.
  8. Best Practices: Apply best practices for organizing model structures to reduce duplication, improve maintainability, create focused single-purpose models, and design clean data architectures that scale with your applications.
  9. Normalizing Data Models: Learn how to design and use normalized data models to decouple integrations, simplify mappings, and enable consistent analytics across systems.