Loading…
Google Cloud Application Integration occupies a specific and useful position in the integration platform landscape. It is not an API gateway — that is Apigee X. It is not a general-purpose workflow engine — that is Cloud Workflows. It is a managed iPaaS designed for connecting applications and orchestrating multi-step data flows with minimal infrastructure management.
We confirm which integration scenarios are appropriate for Google Cloud Application Integration vs. Apigee X or custom connectors. Not every integration belongs on the same platform — this step ensures the right tool is used for each use case.
Authentication model for each connected system, connector configuration, and trigger design — documented before workflow build begins.
Integration workflows built in the Application Integration designer, with data transformation logic, conditional branching, and error handling implemented against the architecture contracts.
Bring us the constraint. We'll bring the team.
It is the right choice when: - The integration scenario involves GCP-native services (Pub/Sub, BigQuery, Cloud SQL, Secret Manager) and the connectors for those services are needed natively - The organization needs visual workflow design without managing a self-hosted integration runtime - The integration scenarios are automation and data synchronization use cases rather than high-throughput, low-latency API proxy scenarios - The team wants to leverage Google's managed connector library to avoid writing and maintaining custom connectors
We implement Google Cloud Application Integration engagements with the same design discipline applied to Apigee X: integration architecture first, implementation against defined contracts, and full error handling and monitoring before production go-live.
Multi-step integration workflows connecting source systems to destination systems, with data transformation steps, conditional branching, parallel execution paths, and loop constructs where needed. Each workflow designed against the data contracts defined in the integration architecture phase.
Google's managed connector library covers over 200 pre-built connectors to common enterprise applications. We configure connectors to Salesforce, ServiceNow, SAP, BigQuery, Cloud SQL, Pub/Sub, Workday, and other systems relevant to the integration scope — with proper authentication setup, connection pooling, and error handling for each.
API triggers for synchronous workflow invocation, Cloud Pub/Sub triggers for event-driven workflows, Cloud Scheduler triggers for scheduled batch workflows, and Salesforce or ServiceNow change-data-capture triggers for system-event-driven scenarios.
Every workflow implements explicit error handling: retry policies with configurable backoff, suspension points for human-in-the-loop error resolution, and dead letter routing for failed workflow executions that require operational investigation.
Cloud Monitoring integration for workflow execution metrics, error rate alerting, and execution duration tracking. Integration with Cloud Logging for full execution trace visibility.
End-to-end testing of each workflow against actual source and destination systems. Error injection testing to validate retry behavior and dead letter routing.
Cloud Monitoring dashboards and alerting for workflow health. Documentation of each workflow's purpose, trigger conditions, and operational procedures. Knowledge transfer to the team responsible for ongoing management.