How does Google Dataflow handle data consistency in distributed data processing?
- Through a combination of record-level acknowledgments and exactly-once processing semantics.
- By implementing a two-phase commit protocol across all worker nodes in the Dataflow job.
- By relying solely on eventual consistency to reduce processing overhead.
- By periodically checkpointing the state of all worker nodes and rolling back in case of inconsistency.
Understanding how Google Dataflow maintains data consistency is crucial for designing robust data processing pipelines. By ensuring that each record is processed exactly once and handling failures gracefully, Dataflow provides a reliable framework for building scalable data applications.
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