AWS

AWS IoT Core now includes a built-in rule action that routes time-series telemetry directly to InfluxDB, eliminating the need for custom device-side code or intermediate processing services. The integration automatically converts incoming data into InfluxDB line protocol and writes it to either Amazon Timestream or a self-hosted InfluxDB cluster. It also supports device-side batching to optimize throughput and reduce costs for high-volume IoT deployments.

• Eliminates custom code or middleware by routing IoT data directly to InfluxDB via native rule actions.
• Auto-converts device payloads to InfluxDB line protocol for seamless ingestion into Timestream or self-hosted clusters.
• Supports device-side batching to optimize cost and throughput for high-volume telemetry streams.
• Simplifies architecture by removing intermediate cloud services typically required for data transformation.

AWS Lambda now allows functions to use complete IAM resource-based policies, replacing the previous limitation where permissions had to be granted individually per principal. This update enables platform admins to define granular access for multiple principals and actions within a single policy document. It also unlocks the full range of IAM condition keys for more precise access control.

• Manage permissions for multiple principals in a single policy document instead of one-by-one.
• Leverage the full suite of IAM condition keys for granular access control on Lambda functions.
• Streamlines permission management for platform admins and security teams at scale.
• Increases flexibility for defining complex access rules compared to the previous per-principal model.

AWS Neuron 2.32 updates the Neuron Kernel Interface to version 0.6.0, adding an on-device top-K instruction and variable-length all-gather support for tensors of differing sizes. The release introduces 13 new library kernels optimized for Mixture of Experts training and sparse attention, including end-to-end MXFP8 support for blockwise MoE layers. Additionally, a new agentic development skill simplifies porting transformer models to the vLLM Neuron plugin.

• NKI 0.6.0 supports variable-size all-gather for ranks with differently sized tensors
• 13 new MoE and sparse attention kernels enable efficient training workflows
• End-to-end MXFP8 forward/backward pass support for blockwise MoE layers
• On-device top-K instruction added to Neuron Kernel Interface
• New agentic skill accelerates transformer model porting to vLLM Neuron plugin

Amazon SageMaker HyperPod now integrates native support for the Ray framework, addressing common operational burdens like job hangs and low GPU utilization. The update includes built-in observability, resilient training capabilities, and managed development environments to streamline workflows. This allows data scientists to create, edit, and monitor Ray workloads directly without relying on complex kubectl commands or static resource allocations.

• Managed Ray environments eliminate the need for manual kubectl operations and static team allocations.
• Built-in observability simplifies monitoring of distributed training and inference workloads.
• Resilient training features help mitigate job hangs and improve cluster efficiency.
• Interactive development tools allow direct code editing without repeated job submissions.

AWS Glue 6.0 is generally available, offering a 30% price reduction alongside support for Apache Iceberg v3, Hudi, and Delta Lake. The update upgrades the runtime to Spark 4.1, Python 3.13, and Scala 2.13. New Iceberg v3 features include VARIANT data types, deletion vectors, and spatial support for improved semi-structured and row-level performance.

• 30% price reduction lowers costs for existing Glue workloads.
• Iceberg v3 adds VARIANT types, deletion vectors, and spatial data support.
• Runtime upgrades to Spark 4.1, Python 3.13, and Scala 2.13.
• Spark Declarative Pipelines simplify orchestration and reduce boilerplate.
• Support for newer versions of Hudi and Delta Lake is now included.