AWS

Amazon Redshift patch P203 enables concurrency scaling for refreshes of Kinesis Data Streams-connected streaming materialized views. This feature allows streaming workloads to automatically scale resources during high-throughput ingestion periods, supporting hundreds of megabytes per second. The update applies to both provisioned clusters and Redshift Serverless workgroups configured with streaming ingestion.

• Streaming MV refreshes now leverage concurrency scaling to handle bursty ingestion loads without manual intervention.
• Apply patch P203 to enable this capability for Kinesis-connected materialized views.
• Reduces latency and storage costs by keeping external data accessible via fast-refreshed views.
• Applies to both managed clusters and Redshift Serverless via existing SQL configuration commands.

A new paper details a cloud-native RAG architecture running entirely on IBM LinuxONE to eliminate data movement for sensitive AI workloads. The system leverages IBM's Spyre PCIe inference cards for generative tasks and Telum II on-chip accelerators for classification, orchestrated via Red Hat OpenShift. This approach keeps data local while addressing latency and regulatory constraints inherent in distributed AI setups.

• Runs full RAG pipeline on LinuxONE, removing network hops for sensitive data.
• Combines Spyre PCIe cards for LLM inference with Telum II for classification.
• Uses Red Hat OpenShift for container orchestration on IBM Z hardware.

Amazon Aurora DSQL now exposes CloudWatch Database Insights metrics, enabling per-statement performance monitoring at the cluster level. The feature captures sampled wait states and normalized SQL statements for active sessions at one-minute intervals. These metrics are available by default at no extra cost and can be analyzed via CloudWatch Database Insights, PromQL, or the Aurora DSQL system diagnostics AI skill.

• Monitor per-statement performance and resource consumption across the entire Aurora DSQL cluster.
• Analyze sampled wait states and normalized SQL to pinpoint the most expensive queries.
• Access data via CloudWatch Database Insights, PromQL, or the built-in AI diagnostic skill.
• Metrics are captured at 1-minute intervals and enabled by default at no additional cost.

Amazon EC2 now offers P6-B300 instances in the Asia Pacific (Seoul) region, featuring 8x NVIDIA Blackwell Ultra GPUs and 2.1 TB of high-bandwidth GPU memory. These instances provide significantly improved networking and memory capacity compared to P6-B200, resulting in faster training times and higher token throughput. They are optimized for training and deploying trillion-parameter foundation models and large language models.

• P6-B300 instances are now available in the Asia Pacific (Seoul) region.
• Each instance includes 8x NVIDIA Blackwell Ultra GPUs with 2.1 TB GPU memory.
• Networking bandwidth is 2x higher than P6-B200, supporting faster distributed training.
• GPU memory and TFLOPS are 1.5x larger, ideal for trillion-parameter models.
• System memory is 4 TB with 300 Gbps dedicated ENA throughput.

AWS has introduced the RDS Switchover Read Replica execution block within ARC Region Switch to automate recovery for Amazon RDS databases using Oracle Data Guard in multi-region setups. Previously, reversing primary and replica roles or promoting a read replica during a regional impairment required manual intervention. This new capability allows for automated orchestration of these database failovers, helping achieve bounded recovery times when regional outages occur.

• Automates Oracle Data Guard role reversal in multi-region RDS deployments via ARC.
• Eliminates manual steps for promoting read replicas during regional impairments.
• Supports bounded recovery time objectives (RTO) for database failovers.
• Integrates database recovery directly into broader application failover orchestration.

Fleet impact: For Oracle Data Guard on RDS, this removes manual switchover steps during regional outages, reducing RTO and human error risk. Ensure your ARC recovery plans are updated to include this new execution block for automated role reversal.

Amazon Redshift now allows extending system table data retention beyond the default 7-day window by integrating with Amazon S3 Tables. The service automatically writes this data to S3 in Apache Iceberg format, handling partitioning and compaction without manual ETL pipelines. This update simplifies compliance, auditing, and long-term observability for data warehouse activities.

• Eliminates need for custom ETL pipelines to archive system table data beyond 7 days.
• Data is stored in Apache Iceberg format within Amazon S3 Tables for queryability.
• AWS manages partitioning, compaction, and retention policies automatically.
• Supports compliance and auditing requirements that exceed standard retention limits.
• Reduces operational overhead for monitoring query performance and warehouse activity.