Releasesby GlyphFor 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 .
Also shipped Sep 24, 2025Google Cloud in September 2025 →
WAF Release - 2025-09-24 - Emergency
This week highlights a critical vendor-specific vulnerability: a deserialization flaw in the License Servlet of Fortra’s GoAnywhere MFT. By forging a license response signature, an attacker can trigger deserialization of arbitrary objects, potentially leading to command injection.
Figma integration for Zapier
Now you can use the Figma integration with Zapier to connect Figma to thousands of other tools without writing code.
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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.
Dataflow now serves a notice for when the Dataflow Runner v2 container image of a streaming pipeline will be...
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 .
Dataflow is available in the Bangkok ( asia-southeast3 ) region
Dataflow is available in the Bangkok ( asia-southeast3 ) region. Learn more about Google Cloud locations .
Dataflow Managed I/O now supports rolling upgrades for streaming jobs
Dataflow Managed I/O now supports rolling upgrades for streaming jobs. With this feature, Dataflow upgrades your Managed I/O connectors in running pipelines as new connector versions become available. For more information, see Automatic upgrades .
Dataflow support for the C4A machine series of Arm processors is now generally available
Dataflow support for the C4A machine series of Arm processors is now generally available. Arm-based VMs are optimized for power efficiency and can provide improved price-performance for many workloads. For more information, see Use Arm VMs on Dataflow .
When you use min_ram or cpu_count resource hints for pipeline steps that don't require accelerators, Auto VM...
When you use min_ram or cpu_count resource hints for pipeline steps that don't require accelerators, Auto VM Selection (Instance Flexibility) is enabled automatically. With Auto VM Selection, workers are provisioned from a curated list of machine types that meet your RAM and CPU requirements.
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 .
Dataflow has updated and expanded its pipeline update features for streaming jobs: - Automated...
Dataflow has updated and expanded its pipeline update features for streaming jobs: Automated stop-and-replace updates : You can perform automated, declarative stop-and-replace updates to streaming jobs. Parallel updates with the same job name : When you perform automated parallel updates, you can use the same job name for the new replacement job.