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Dataflow now serves a notice for when the Dataflow Runner v2 container image of a streaming pipeline will be...

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Dataflow now serves a notice for when the Dataflow Runner v2 container image of a streaming pipeline will be upgraded. To use a new image and avoid the scheduled maintenance, launch a replacement job before the upgrade. For more information, see Runner v2 harness update .

StreamingData integration

Read on docs.cloud.google.com

Also shipped Jan 13, 2026Google Cloud in January 2026 →

WebM Container Support

Flux now accepts WebM containers with Opus codec encoding: WebM containers with opus encoding When sending WebM containerized audio, omit the encoding and sample_rate parameters, Flux will automatically detect these from the container metadata.

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Why This Matters

WebM is commonly used in web applications and streaming scenarios. This addition makes it easier to integrate Flux with audio sources that natively output WebM format, eliminating the need for format conversion.

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More from DataflowEvery Dataflow release →

Dataflow now supports speculative execution for batch pipelines

Dataflow now supports speculative execution for batch pipelines. This feature mitigates the impact of slow-running tasks ( stragglers ) by launching a redundant execution of these tasks. The first task to finish is used, and the other is canceled, which can improve the overall completion time of your pipeline. This feature is generally available.

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Dataflow job builder now supports external Iceberg REST Catalogs as a source

Dataflow job builder now supports external Iceberg REST Catalogs as a source. You can now ingest data from external Apache Iceberg REST catalogs (IRC) directly into Lakehouse tables using Dataflow's job builder UI without writing code. For more information, see Import data from external Iceberg catalogs to Lakehouse using Dataflow .

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For jobs that use GPUs, Dataflow now supports the flex-start provisioning model

For jobs that use GPUs, Dataflow now supports the flex-start provisioning model. This flex-start provisioning model can improve your ability to get access to constrained GPU resources for short-duration workloads. This feature is available in Preview and is for batch pipelines only. For more information, see Configure a provisioning model .

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