Preface
Introduction to Informatica Connector Toolkit
- Supported features
- Informatica Connector perspective
  - Connector Navigator
  - Connector Progress
Before you begin
- Download the welcome kit
- Download and install Eclipse IDE
- Download and install the Informatica Connector Toolkit
- Download OpenJDK and configure environment variables
- Log in to Informatica Intelligent Data Management Cloud
Develop a connector for Cloud Data Integration
- Create a new connector project
- Phase 1: Establish a connection
  - Create a connection
  - Test a connection
- Phase 2: Define the metadata
- Phase 3: Configure the runtime behavior
- Phase 4: Generate the connector package
  - Naming Convention
- Phase 5: Generate and run the test scenarios
  - Generate the test scenarios
    - Configure parameters in test suite file
    - Regenerate a test case
  - Run the test scenarios
    - Unit tests and sanity tests
    - Integration tests
Develop a connector for Data Loader
- Create a new connector project
- Phase 1: Establish a connection
  - Create a connection
  - Test a connection
- Phase 2: Define the metadata
- Phase 3: Configure the runtime behavior
- Phase 4: Generate the connector package
  - Naming Convention-DLT
- Phase 5: Generate and run the test scenarios
  - Generate the test scenarios
    - Configure parameters in test suite file
    - Regenerate a test case
  - Run the test scenarios
    - Unit tests and sanity tests
    - Integration tests
Import a connector
Connection attributes
- Connection attribute properties
Type system
- Native types and semantic categories
  - Native Type Properties
- Cloud Data Integration types
Connector metadata
- Metadata components
- Import dialog box settings
Partitioning capability
- Automatic partitioning
- Static partitioning
Pushdown capability
- Pushdown optimization execution flow
- Classes and methods for pushdown capabilities
Mappings in advanced mode
- Project, classes, and methods
Manual changes to Informatica Connector Toolkit source code
- Code changes for connection pooling
  - Connection Pooling through API
- Code changes for custom query
- Code changes for object path override
- Code changes for pushdown capability
- Code changes for SQL transformation
- Code changes for stored procedures
Runtime behavior
- Runtime Java functions
Connector example: MySQL_Cloud
- MySQL_Cloud Connector requirements
- Building the sample connector
- MySQL_Cloud Connector components
Version control integration
- Git version control integration
  - Prerequisites
  - Build a connector in Git repository
- Perforce version control integration
Appendix A: Metadata models
- Metadata model components
- Metadata patterns
- Features of type A metadata template
Appendix B: ASO model
- ASO model components
- ASO projections
Appendix C: Connector project migration
- Migrate connector project from Windows platform to other platforms
Appendix D: Frequently used generic APIs in Informatica Connector Toolkit
Appendix E: Frequently asked questions

Cloud Data Integration Connector Toolkit Developer Guide

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Metadata patterns

The metadata pattern of a connector describes the metadata structure of the native data source. You can choose a metadata pattern template to define the metadata pattern of a connector.

A metadata pattern template is a set of packages such as catalog, container, flat record, and so on. Sets of related classes that define the metadata pattern are organized as packages.

Though a connector can have more than one metadata pattern, the Informatica Connector Toolkit currently supports only Type A template. The type A template provides the metadata patterns for flat records. You can use the metadata catalog that the Type A template provides to describe the metadata structure of the data source.

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Cloud Data Integration Connector Toolkit Developer Guide

Cloud Data Integration Connector Toolkit Developer Guide

Metadata patterns

Metadata patterns