AWS

AWS has launched a public preview of AWS Interconnect, a purpose-built service enabling multicloud connectivity between AWS and Microsoft Azure. This offering addresses the complexity of DIY global multi-layered networks by providing a dedicated path for workloads to traverse between the two providers. The service supports multicloud strategies driven by interoperability needs and the desire to deploy applications across diverse environments.

• Eliminates need for DIY global multi-layered network management
• Enables direct connectivity between AWS and Microsoft Azure
• Simplifies multicloud deployment and interoperability workflows
• Currently available as a public preview feature

AWS has enabled Kinesis Data Streams to write data directly to standard Amazon S3 buckets, eliminating the need for custom consumers or complex service chains. This native integration simplifies ingestion for batch analytics, log storage, and compliance retention while cutting delivery costs by up to 60% compared to self-managed alternatives. The update allows practitioners to offload pipeline management to a serverless mechanism without configuring additional compute resources.

• Eliminates need for custom consumer applications or chaining multiple serverless services for S3 delivery.
• Reduces data delivery costs by up to 60% versus self-managed compute alternatives.
• Simplifies architecture for batch analytics, log delivery, and long-term compliance retention.
• Leverages Kinesis serverless scaling to handle high-throughput streams into standard S3 storage.

Amazon OpenSearch Service has expanded its Cluster Insights feature with 17 new checks to automatically identify root causes for Red and Yellow cluster statuses. These insights now cover resource exhaustion issues like JVM out-of-memory and CPU saturation, as well as configuration problems such as zone imbalance and incorrect replica counts. The service replaces manual metric correlation with automated detection and tailored recommendations to resolve unassigned shards more quickly.

• Reduces mean time to resolution for cluster health issues by automating root cause detection.
• Covers key failure modes including JVM OOM, CPU saturation, zone imbalance, and replica misconfigurations.
• Provides specific remediation advice, such as scaling instance types, directly in the insights.
• Eliminates the need for manual cross-node and cross-AZ metric correlation during outages.

Amazon Aurora Serverless now offers up to 30% better performance and improved scaling algorithms in Asia Pacific (New Zealand, Thailand), Africa (Cape Town), Europe (Milan), and Mexico (Central). These enhancements apply to both Aurora PostgreSQL and Aurora MySQL, optimizing resource handling for competing tasks like busy web apps and API services. The update positions the service as a strong fit for agentic AI workloads characterized by traffic bursts and unpredictable idle periods.

• Performance uplift of 30% applies to both PostgreSQL and MySQL serverless engines.
• New regions include NZ, Thailand, Cape Town, Milan, and Central Mexico.
• Smarter scaling handles resource competition better for web and API workloads.
• Ideal architecture for agentic AI apps with bursty, unpredictable traffic patterns.
• Pay-per-use model remains, scaling to zero when idle to minimize cost.

Amazon Kinesis Data Streams now offers streaming tables, a serverless feature that continuously writes data from Kinesis streams to Apache Iceberg tables on S3 Tables. This capability removes the operational burden of building self-managed Iceberg delivery pipelines and claims up to 50% reduction in data delivery costs. To optimize performance and cut query expenses by up to 30%, the service includes intelligent inline compaction that resolves the small file problem automatically.

• Eliminates self-managed Iceberg pipelines by offering fully serverless stream-to-table delivery.
• Reduces data delivery costs by up to 50% through managed infrastructure.
• Inline compaction prevents small file overhead, keeping query performance predictable.
• Lowers downstream query costs by up to 30% via optimized file sizing.
• Enables near real-time analytics and AI/ML feature pipelines with fresher data.