Google Cloud Data Fusion

Cloud Data Fusion is built on CDAP and separates instance management, pipeline design and pipeline execution. Source, transformation and sink stages describe how records move and change; connectors and plugins expose supported systems. Runtime compute, service accounts and networking determine whether the designed pipeline can execute and access its data. Visual authoring reduces the need to write every integration from scratch, but it does not remove schema, partitioning or transformation semantics. Data Fusion is an integration platform rather than a model training or inference service.

The practitioner checks connector capabilities, defines input and output schemas and documents how stages handle malformed or missing records. They configure runtime identities and network access, choose execution resources and test on representative samples. They inspect lineage, counts and reconciliation checks and design reruns so interrupted processing does not duplicate or partially publish data. Useful work yields a repeatable integration with known dependencies and recovery behavior, including operational evidence about runtime consumption rather than only a saved design in the interface.

A connector can transfer data correctly while a transformation changes its business meaning. Schema drift, access changes and plugin behavior can break execution or silently alter output. Instance and runtime resources may have separate cost implications. Check current connector support, permissions, lineage and reconciliation. Visual pipelines require the same attention to data correctness and lifecycle as code-based pipelines; interface convenience is not evidence that the resulting dataset is fit for training.