Releasesby GlyphYou can now use Hyperdisk Balanced disks for Dataflow worker VMs
You can now use Hyperdisk Balanced disks for Dataflow worker VMs. With Hyperdisk Balanced disks, you can provision IOPS and throughput independently of disk size by using the diskProvisionedIOPS and diskProvisionedThroughput pipeline options (Java SDK) or disk_provisioned_iops and disk_provisioned_throughput_mibps pipeline options (Python and Go SDKs).
Also shipped Jun 22, 2026Google Cloud in June 2026 →
SDK Releases: MCP Server
- v0.10.0 - Added Music v2 model and endpoints, MCP tool annotations for all 24 tools, Python 3.14 support, Singapore data residency, EU/IN residency shorthand aliases, graceful shutdown handling for client disconnections, and fixes for voice preview creation and output directory path resolution.
What if your product built itself?
PostHog has released a pipeline that ingests product signals, uses research agents to identify actionable problems, and automatically opens GitHub pull requests.
Preview data changes in Netlify Database
You can now preview proposed data changes for your Netlify database with a Git-style diff view, then apply part or all of those changes to a production version of your database.
Refresh Token metadata is now Generally Available
We're excited to announce that **Refresh Token Metadata** is now **Generally Available** for Enterprise customers. Refresh Token Metadata allows you to attach custom key-value pairs to refresh tokens, enabling richer context storage and more personalized authentication experiences.
Neo code reviews: AI code review for infrastructure
As of today, Pulumi Neo code reviews are available in public preview. Building on the Pulumi GitHub app , which already posts rich diffs as PR comments on pull requests, Neo code reviews help you understand not just what’s changing, but also how those changes relate to, and potentially impact, the rest of your running infrastructure.
More from DataflowEvery Dataflow release →
Dataflow now supports NVIDIA RTX Pro 6000 GPUs
Dataflow now supports NVIDIA RTX Pro 6000 GPUs. You can use this GPU model to run your Apache Beam pipelines on Dataflow. RTX Pro 6000 GPUs are recommended for large, medium, and small model inference workloads. To configure your workers with this GPU model, set the accelerator type to nvidia-rtx-pro-6000 .
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.
You can now pause a Dataflow batch job using the pause_on_failure service option
You can now pause a Dataflow batch job using the pause_on_failure service option. This feature lets you preserve the state of your batch pipeline job, address external issues, and resume processing without losing completed work. You can use this option to automatically pause a job on failure, or use this feature to manually pause a job when you chose to.
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 .
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 Auto VM Selection now supports 4th generation Compute Engine machine series
Dataflow Auto VM Selection now supports 4th generation Compute Engine machine series. When Auto VM Selection is enabled, the service prioritizes these newer generation VMs for improved performance.
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 .
Dataflow now supports streaming high availability for Streaming Engine jobs ( Preview )
Dataflow now supports streaming high availability for Streaming Engine jobs ( Preview ). When you enable the requires_high_availability service option, Dataflow replicates backend state across multiple zones in a region to protect your job from single-zone failures. For more information, see Streaming high availability .