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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Partitioning capability

You can use the Informatica Connector Toolkit to specify the partition type and implement the partition logic to use when the Cloud Data Integration reads or writes data.

Based on the partition logic you implement for a connector, the Data Integration Service dynamically divides the underlying data into partitions and processes all of the partitions concurrently.

You can specify the following partitioning types for a connector:

Dynamic: A partitioning logic that determines the number of partitions at run time based on the partition information from the data source. If the data source provides partition information, you can use dynamic partitioning to increase the performance of connector read and write operations.
Static: A partitioning logic that is based on the partition information that the user specifies, such as number of partitions or key range. If you require the user to specify the partition information, you can implement fixed partitioning for the connector. If the tables in the data source support key range partitioning, you can add key range partitioning capability for the connector.

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Cloud Data Integration Connector Toolkit Developer Guide

Cloud Data Integration Connector Toolkit Developer Guide

Partitioning capability

Partitioning capability