Releasesby GlyphDataflow 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.
Also shipped Nov 21, 2025Google Cloud in November 2025 →
What's new in Sigma
Admin Add a key file for Snowflake key pair connections When configuring a Snowflake connection, you can add a PKCS#8-formatted key file ( .p8 ) instead of pasting your private key when setting up key pair authentication for the connection or an OAuth service account. For more details, see Connect to Snowflake .
Containers - New CPU Pricing for Containers and Sandboxes
Containers and Sandboxes pricing for CPU time is now based on active usage only, instead of provisioned resources. This means that you now pay less for Containers and Sandboxes. An Example Before and After Imagine running the standard-2 instance type for one hour, which can use up to 1 vCPU, but on average you use only 20% of your CPU capacity.
Notifications, Conductor Queue
This week is all about staying ahead of issues: Notifications graduate from Priority Boarding to General Availability, giving everyone a real-time view of what’s happening across projects and environments. There’s also a new way for our community Conductors to triage questions faster.
Projects deployed using a zip file via API now support branch deploys
When you deploy a project using a ZIP file and the Netlify API, you can now also create branch deploys using the new branch parameter. While it was always possible to pass a branch parameter to the https://api.netlify.com/api/v1/sites/{site_id}/builds endpoint, it previously had no effect for deploys made programmatically using the API.
Faro Web SDK v2.0 is now GA
This major release brings new updates to User Actions for GA, introduces improvements to Web Vitals, simplifies configuration, cleans up deprecated internals, and adds experimental navigation instrumentation for soft navigations. Highlights: User Actions are now GA Additional configuration to manage action state Added an importance attribute (e.g.
Custom environments for EAS Environment Variables
EAS Environment variables now support custom environments on top of the default development, preview, production. Available for production and Enterprise plans.
More from DataflowEvery Dataflow release →
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 .
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 .
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.