Pre-built connector vs custom connectorPre-built connectors, also called managed connectors, provide out-of-the-box integration with core banking systems and third-party services. Backbase develops and maintains these connectors.You build a custom connector on the Grand Central platform to integrate with systems that don’t have a pre-built connector available.For more information, see Platform terminology.
Prerequisites
Before you begin, ensure you are comfortable with the following technologies:Infrastructure
Familiarity with Kubernetes and Git/GitHub workflows.
Development
Java development and XML Schema (XSD) knowledge.
Data transformation
API request/response mapping using JOLT and XSLT.
Required access
Ask the Grand Central team to grant you:- Repository access: View and clone access to the
gc-applications-liverepository. - APIM subscription key: A subscription key for the Grand Central Unified API and product sandbox.
Included features
Grand Central provides platform capabilities and connector-specific resources.Platform capabilities
The Grand Central iPaaS platform provides the following capabilities. For more information, see Grand Central iPaaS.Pre-built connector resources
Pre-built connectors include the following resources. These resources aren’t available for custom connectors unless you build them yourself.Configurable features
You can configure the following features for your connector deployments:Pagination
You can paginate bulk data requests through the following Grand Central APIs:- Search party
- Deposit transactions
- Loan transactions
Repository structure
Thegc-applications-live repository holds the configuration for iPaaS and connector deployments in dev, test, and prod. The following diagram shows the typical folder structure.
Reference architecture
Configuration logic
Connector configuration uses a hierarchical override system that lets you define defaults and customize per environment.Application YAMLs
Configurations in the/apps directory follow a top-down override hierarchy. Settings are merged in order, meaning subsequent files take precedence over previous ones. This allows you to define global environment defaults and selectively override them at the connector-version level.
A standard deployment descriptor includes:
- Namespace: The Kubernetes isolation layer for the deployment.
- Version Control: Links to the source and Azure Container Registry (ACR) repositories.
- Templating: The Helm chart used to standardize the deployment.
- Orchestration: ArgoCD Sync Policies for automated reconciliation.
- Data Resources: Configurable JOLT or XSLT files for message transformation.
Sample apps YAML file
View sample application YAML (ArgoCD)
View sample application YAML (ArgoCD)
Values YAML file
Use these files to define connector properties, telemetry, and data transformation logic.Sample values YAML file
View sample values and Jolt transformation
View sample values and Jolt transformation
Map data fields
To ensure accurate data transfer and interoperability between Grand Central and the systems your connectors integrate with, such as core banking platforms, third-party services, or fintech APIs, map data fields in the Grand Central Unified API specification to the fields those systems use.The following steps describe how to edit existing mappings. To add custom fields, see Custom fields.
Prerequisites
Before you start mapping, complete the following:- Verify whether the integrated system supports adding custom fields.
- Review the available fields in both Grand Central and the integrated system to identify gaps or discrepancies.
- Determine whether you need any additional fields in the integrated system, then integrate them.
- Identify which custom fields are necessary and map them in the Grand Central transformation source file.
Map fields
To map data fields, connectors use JOLT or XSLT transformations depending on the inbound data format accepted by each target system:- JOLT transformations: JSON inbound
- XSLT transformations: XML inbound
common-templates.xslt or credentials-and-device-info.xslt, as in some Jack Henry Symitar and Fiserv DNA connectors. The files available depend on the connector you use. Add the transformation file content your deployment needs to the values.yaml file in the sources section of your live project repository.
JSON fields
To map data fields that use JSON data formats, use the JOLT transformation library. To add or update a mapping, edit the JOLT transformation source file.Example mapping scenarios
The following table shows example mapping scenarios for fields in Grand Central and the integrated system:Field available in Grand Central only
Field available in the target system only
Field unavailable in both systems
Custom JOLT transformations
The Grand Central custom JOLT transformation operation is available in the Grand Central generic connector library. You can use it to build custom data transformations that align JSON data from the integrated system with the Grand Central output format.Available transformations
The following table lists the available Grand Central custom JOLT transformation operations:
The DateTime transformation converts incompatible DateTime values from target systems to UTC format in the Grand Central output. To use this transformation, specify the source date fields that require conversion and provide the preferred DateTime format.
Example DateTime transformation:
XML fields
To map data fields that use XML data formats, use the XSLT transformation library. To add or update a mapping, edit the XSLT transformation source file.Example mapping scenarios
The following table shows example mapping scenarios for fields in Grand Central and the integrated system:XML field in both systems
XML field in the target system only
Custom fields
Custom fields let you access data or attribute values that the Grand Central Unified API may not cover. Use custom fields to extend the APIs to suit your specific requirements. Grand Central core connectors include predefined fields with a wide range of attributes for each core. If you need additional fields beyond those predefined, add them to thecustomFields object.
Supported data types
A custom field is represented as aMap<String, Object> and supports the following data types:
Link custom attributes
To link your custom attributes to core attributes, edit the mapping files. Manage this configuration in thevalues.yaml file in the sources section of your live project repository. For instructions about mapping custom fields between Grand Central and the core banking system, see Map data fields.
Examples
Valid custom field structure
The following example includes five custom field attributes and their corresponding values:Invalid custom field structure
The following example is invalid due to its complexity. Theaddress field contains a nested object, which is not supported: