AWS

Amazon Aurora Serverless now offers up to 30% improved performance and enhanced scaling algorithms across five additional AWS Regions. The update applies to both Aurora PostgreSQL and Aurora MySQL, targeting workloads with unpredictable traffic like agentic AI applications. The system continues to scale down to zero when idle, ensuring cost efficiency for bursty or intermittent demand patterns.

• Performance gains apply to both Aurora PostgreSQL and MySQL serverless engines.
• Available in New Zealand, Thailand, Cape Town, Milan, and Central Mexico.
• Smarter scaling handles resource competition in busy web and API services.
• Ideal for agentic AI apps with traffic bursts and unpredictable idle periods.
• Cost model remains pay-per-use with scaling to zero during inactivity.

Amazon Redshift now reads and writes Apache Iceberg v3 tables, introducing default column values, row lineage, and deletion vectors. Default values simplify schema evolution by applying initial values when data is missing. Row lineage enables incremental pipelines and CDC workflows by tracking row identity and update sequences. Deletion vectors replace positional delete files with compact compressed bitmaps for improved efficiency.

• Default column values simplify schema evolution when adding columns to existing tables.
• Row lineage pseudo-columns enable efficient incremental pipelines and CDC workflows.
• Deletion vectors use compact compressed bitmaps, replacing v2 positional delete files.
• Redshift now fully supports reading and writing Iceberg v3 tables in data lakes.

Amazon EC2 has generally available the R9g and R9gd memory-optimized instances, driven by fifth-generation AWS Graviton5 custom CPUs. R9g targets memory-intensive workloads like databases, caches, and big data analytics, while R9gd adds local NVMe-based SSD storage for those requiring high-speed block-level access. These instances aim to deliver superior price-performance for Linux-based applications, including containerized microservices and various programming languages.

• Graviton5 CPUs now power R9g/R9gd, offering best-in-class price performance for memory-intensive tasks.
• R9gd instances include local NVMe SSDs, suitable for workloads needing fast block-level storage.
• Optimized for databases, in-memory caches, real-time analytics, and containerized Linux apps.
• Supports major languages including C/C++, Rust, Go, Java, Python, and .NET Core.

AWS has added support for in-place major version upgrades (MVU) for Amazon DocumentDB, allowing clusters running engine versions 3.6 or 4.0 to jump directly to version 8.0. This capability eliminates the requirement for sequential intermediate upgrades, preserving data and cluster configurations during the transition. The new version provides updated security patches, performance enhancements, and fresh developer features.

• Eliminates multi-step upgrade paths by allowing direct jumps from 3.6 or 4.0 to 8.0.
• In-place upgrades preserve existing data, configurations, and cluster settings without migration.
• Access to latest security patches and performance improvements in engine 8.0.
• Feature is available in all AWS regions where older versions are currently supported.
• Review MVU documentation for detailed upgrade guidance and version differences before proceeding.

Amazon Redshift now allows provisioned clusters and serverless workgroups to authenticate using AWS IAM Identity Center tokens. When configured with Enhanced VPC Routing (EVR), all traffic remains within the AWS network, traversing the VPC for strict governance. This setup enables single sign-on with corporate credentials while ensuring no public internet egress, satisfying data residency and regulatory isolation requirements.

• Enables SSO via IAM Identity Center for Redshift provisioned clusters and serverless workgroups.
• Enhanced VPC Routing ensures traffic stays within the VPC, eliminating public internet exposure.
• Facilitates compliance with strict data residency and network isolation mandates.
• Security is enforced through VPC security groups, NACLs, and endpoint policies.
• Traffic visibility is maintained via VPC Flow Logs for comprehensive auditing.

Amazon Kinesis Data Streams now supports streaming tables, a serverless feature that continuously writes data from Kinesis to Amazon S3 Tables in Apache Iceberg format. This capability removes the need for self-managed delivery pipelines, cutting data delivery costs by up to half. Inline compaction addresses the small file problem to maintain predictable query performance and reduce downstream query expenses by up to 30 percent.

• Eliminates operational overhead of self-managed Iceberg ingestion pipelines.
• Reduces data delivery costs by up to 50% via serverless architecture.
• Inline compaction mitigates small file issues and stabilizes query performance.
• Optimized for near real-time analytics and AI/ML feature store pipelines.