The dataflow model¶
Akidau, T., Bradshaw, R., Chambers, C., Chernyak, S., Fernández-Moctezuma, R. J., Lax, R., McVeety, S., et al. (2015). The dataflow model. Proceedings of the VLDB Endowment.
Cited by¶
1 citation across 1 artifact.
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Domain-specific¶
- Stream Processing
- Frameworks such as Apache Flink and Google's Dataflow model (Akidau et al. 2015) made this explicit, giving engineers a formal vocabulary for window types (tumbling, sliding, session), trigger policies, and exactly-once delivery semantics under failure and restart.
This sourceThe Dataflow Model paper proposing principled event-time windowing abstractions for unbounded, unordered data, with explicit correctness, latency and cost tradeoffs. The Dataflow Model's account of unbounded, unordered data in which results may be revised when old data are retracted, under an explicit correctness-latency-cost tradeoff.
Supported in partVerified against the publisher's abstract
“consumers of these datasets have evolved sophisticated requirements, such as event-time ordering and windowing by features of the data themselves”
- Frameworks such as Apache Flink and Google's Dataflow model (Akidau et al. 2015) made this explicit, giving engineers a formal vocabulary for window types (tumbling, sliding, session), trigger policies, and exactly-once delivery semantics under failure and restart.
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