AWS

AWS Lambda now allows functions to explicitly choose between Amazon S3 Files high-performance storage and standard S3 buckets for input data. This configuration enables teams to optimize read throughput and latency based on specific application needs, such as low-latency file system access or high-throughput large reads. S3 Files automatically routes operations to the storage tier best suited for the request, combining performance with S3 durability and cost benefits.

• Lambda functions can now explicitly target S3 Files or S3 buckets for input data.
• Optimize read latency with S3 Files or high throughput with standard S3 buckets.
• S3 Files routes operations automatically to the best storage tier for performance.
• Enables scalable data pipelines and stateful agentic workloads with file system simplicity.
• Default behavior remains unchanged unless explicitly configured by the user.

AWS Graviton-powered Redshift RG instances have launched in the Zurich region, offering up to 2.4x better performance than RA3 instances at a 30% lower cost per vCPU. These instances utilize a custom vectorized query engine to process Apache Iceberg and Parquet data directly on cluster nodes. Existing RA3 clusters can be upgraded to RG using Snapshot & Restore, Elastic Resize, or Classic Resize methods.

• RG instances in Zurich deliver 2.4x speedup and 30% cost reduction vs RA3.
• Unified engine handles Redshift warehouse and data lake workloads via Iceberg/Parquet.
• Available in rg.large, rg.xlarge, rg.4xlarge, and rg.12xlarge sizes.
• Upgrade paths include Snapshot & Restore, Elastic Resize, or Classic Resize.

AWS Lambda Managed Instances now support a 90-minute function timeout for asynchronous and event source mapping invocations, up from the previous 15-minute limit. This sixfold increase allows developers to run data-intensive workloads like media transcoding, financial simulations, and AI inference without architectural workarounds. The change eliminates the need to re-architect applications for jobs requiring continuous execution within the serverless model.

• LMI timeout increased 6x to 90 minutes for async and ESM invocations
• Eliminates need for workarounds in long-running batch or AI inference tasks
• Supports data-intensive jobs like Monte Carlo simulations and media transcoding
• No re-architecture required for existing serverless apps needing longer execution

Amazon EBS Volume Clones now allows copying volumes between AWS accounts while re-encrypting them using a KMS key in the destination account. This feature enables organizations to securely move production data into isolated development accounts for safe experimentation. It also supports compliance requirements that mandate separate encryption keys for production and non-production environments.

• Clone production DB volumes directly into dev accounts without manual export/import steps.
• Enforce key separation by re-encrypting data with target account KMS keys during copy.
• Simplifies secure data sharing across account boundaries for testing and analysis.
• Reduces risk of exposing production data in non-isolated environments.

AWS has introduced a native data source connector for Confluence Data Center within Amazon Bedrock Managed Knowledge Base. This fully managed RAG service now automatically crawls, extracts metadata, and performs incremental syncs from self-hosted Confluence instances. Users can ingest wiki pages and blog posts by simply providing instance credentials, eliminating the need for custom ingestion pipelines.

• Eliminates custom ETL pipelines for self-hosted Confluence Data Center integration
• Automates crawling, metadata extraction, and incremental syncs via native connector
• Enables AI agents to access institutional knowledge from Confluence spaces and blogs
• Requires only instance credentials to start ingesting content into Bedrock Knowledge Base