AWS

Amazon Aurora DSQL now supports multi-Region clusters in Europe (Stockholm), Europe (Spain), Asia Pacific (Mumbai), and Asia Pacific (Singapore). This service provides a serverless, distributed SQL database with active-active high availability and multi-Region strong consistency. Each cluster offers writable endpoints in both peered regions, ensuring a single logical database remains available even if one region fails.

• Aurora DSQL is now available in four new regions for multi-Region cluster deployments.
• Active-active architecture ensures strong consistency across geographically distributed nodes.
• Writers can target either region, maintaining availability during regional outages.
• Expands the footprint beyond US and existing APAC regions to new European markets.

Amazon RDS for Oracle now allows 1-year and 3-year Reserved Instances for R8i and M8i instance types, offering up to 53% savings over On-Demand pricing. These instances utilize custom Intel Xeon 6 processors exclusive to AWS, providing significantly higher memory bandwidth and performance compared to previous generations. Reserved Instance benefits apply to both Single-AZ and Multi-AZ configurations, allowing flexible workload management within the same instance class.

• Secure up to 53% cost reduction by reserving R8i or M8i instances for 1 or 3 years.
• Leverage custom Intel Xeon 6 processors for 2.5x memory bandwidth and 15% better price-performance.
• Maintain flexibility to switch between Single-AZ and Multi-AZ within the same instance class.
• Ideal for stable production workloads requiring high memory throughput and Oracle compatibility.

Fleet impact: For Oracle ExaCC/RAC and RDS fleets, this enables significant OpEx reduction on memory-intensive workloads by locking in R8i/M8i rates. Plan migrations to these new instance classes to capitalize on the superior memory bandwidth, ensuring your reservation strategy covers both Single-AZ and Multi-AZ deployments for maximum flexibility.

AWS has integrated the IAM Policy Simulator directly into the IAM console, replacing the standalone tool. This update enables testing of Service Control Policies (SCPs) alongside identity and resource policies. The change allows engineers to validate how organizational policy hierarchies interact with specific permissions before deployment.

• Simulator is now native to the IAM console, centralizing identity workflows.
• New SCP testing capability validates organizational policy constraints.
• Improved scenario modeling supports more accurate pre-deployment checks.
• API access remains for automated validation of condition keys.

Amazon Redshift Data API now supports long polling via WaitTimeSeconds, allowing clients to wait synchronously for SQL statement completion rather than repeatedly polling. It introduces ListSessions to enumerate and filter active sessions by status, compute target, or database. Additionally, batch execution now supports flexible transactions, enabling statements within a batch to run on separate transactions.

• Use WaitTimeSeconds on ExecuteStatement or DescribeStatement to reduce API chatter and simplify async workflows.
• Leverage ListSessions to audit active connections, filter by compute target, or manage stale sessions.
• Split batch statements across separate transactions for better isolation and error handling flexibility.
• Ideal for serverless or event-driven architectures needing tighter control over Redshift query lifecycles.

Amazon S3 Tables now accepts the Variant data type per the Apache Iceberg V3 specification, enabling direct ingestion of semi-structured JSON without predefined schemas. Compatible engines automatically shred this data into hidden columns, generating Parquet statistics that facilitate query optimizations like file pruning. The service also handles ongoing maintenance tasks such as compaction to consolidate small files into larger, more efficient structures for analytical workloads.

• Ingest JSON directly into S3 Tables without defining a fixed schema upfront.
• Iceberg V3 engines shred Variant data into hidden columns for automatic stats generation.
• Leverage Parquet statistics to enable query optimizations like file pruning.
• Automatic compaction consolidates small Variant files into larger, readable chunks.