How Multi-Cloud Data Integration Actually Works

The Architecture in Practice: Real multi-cloud integration appears when different business capabilities live in different clouds. Imagine user-facing microservices in AWS responding at 5 to 10 milliseconds p50 latency, ad targeting and machine learning in GCP, and enterprise analytics in a multi-cloud warehouse like Snowflake or Databricks. The Event Flow: User actions hit AWS APIs and immediately publish events to a cloud-neutral event bus operated by a streaming provider like Confluent with clusters in multiple clouds. Within 50 to 100 milliseconds p99, those events replicate to a GCP region where ML models consume them for recommendations. This is not hypothetical: systems routinely process tens of thousands of events per second this way. Separately, Change Data Capture processes in AWS stream transactional updates like orders and payments into the same event backbone. Downstream, micro-batches of 1 to 5 minutes ingest these streams into multi-cloud warehouses. Data volumes commonly reach 1 to 10 terabytes per day. Headless Data Architecture: Modern platforms like Databricks support a headless approach. Data lives in cloud object stores (S3, Google Cloud Storage, Azure Blob), but access is unified through a common catalog and governance layer. A query engine like Trino reads from both AWS and GCP object stores using data virtualization, minimizing duplication. Cross-cloud analytics queries typically complete in 2 to 10 seconds at p95. That is acceptable for business intelligence dashboards but far too slow for user-facing APIs. This creates a natural separation: operational queries stay local, analytical queries federate across clouds. The Orchestration Layer: An Integration Platform as a Service (iPaaS) provides operational control. It auto-scales ingestion workers across regions, monitors pipeline lag and error rates, and enforces governance constraints. For example, it ensures Personally Identifiable Information (PII) for European Union users never leaves EU regions even when compute spans multiple clouds. The result is composable architecture. Each cloud handles its strengths. Data flows are governed centrally with clear SLAs and blast radius containment. When one cloud experiences issues, the system degrades gracefully rather than failing completely.