AWS Updates 2026-10-08 | Cloud Provider News

AWS finally makes cross-Region data lake queries much easier

Amazon Redshift now supports cross-Region queries for your data lake. Amazon Redshift can now query Amazon S3 data lake tables in another AWS Region without copying or replicating the data first. That’s a big deal for teams running analytics across regions, since it cuts out extra data movement before a query even starts.

AWS makes Batch usage easier to monitor in CloudWatch

AWS Batch now publishes job metrics to Amazon CloudWatch. AWS Batch now emits job lifecycle metrics directly to Amazon CloudWatch, so teams can see queue health, failure rates, and job durations natively. That’s a straightforward observability upgrade for batch environments.

AWS adds more control to EKS Auto Mode for performance-sensitive workloads

Amazon EKS Auto Mode now supports advanced compute configuration. EKS Auto Mode now allows kubelet, Linux kernel, sysctl, and hugepages tuning directly in NodeClass, while still handling provisioning, scaling, patching, and upgrades. That means you get more control without giving up the automation.

AWS gives EC2 AMI owners better tag sharing across accounts

Amazon EC2 introduces shared tags for Amazon Machine Images. Amazon EC2 now lets AMI owners share selected tags with every account an AMI is shared with. That removes some of the need for custom tag replication workflows.

AWS Security Hub changes how runtime monitoring is billed

AWS Security Hub now includes GuardDuty Runtime Monitoring in Threat Analytics pricing. GuardDuty Runtime Monitoring is now bundled into the AWS Security Hub Threat Analytics plan, with billing consolidated under Security Hub instead of separate GuardDuty charges. That changes how the service is metered and tracked.

AWS Well-Architected Agent enters preview with cost-focused recommendations

AWS Well-Architected Agent enters preview. AWS Well-Architected Agent is a new AI-powered service that analyzes infrastructure across cost, security, performance, and reliability, then returns prioritized recommendations. It pulls together metrics, topology, and template data to do that analysis.

AWS adds filtered export for DynamoDB to reduce data movement

Amazon DynamoDB introduces filtered export to Amazon S3. DynamoDB filtered export lets you export only the items and attributes you need, instead of exporting whole tables. That makes export workflows more targeted.

The direct benefit is lower data movement and storage costs. It can also make analytics and recovery workflows more efficient because you’re only moving the data that matters.

AWS gives Aurora and RDS customers new AMD-based instance options

Amazon Aurora and RDS now support AMD-based R8a instances. Amazon Aurora and Amazon RDS now support R8a database instances powered by 5th generation AMD EPYC processors. That adds another instance family choice for database workloads.

This matters for right-sizing and price-performance planning. Having another instance option can make it easier to match workload needs to the right spend profile.

AWS RDS adds more AMD-based capacity options for common databases

Amazon RDS now supports AMD-based M8a instances. Amazon RDS for PostgreSQL, MySQL, and MariaDB now supports M8a database instances powered by 5th generation AMD EPYC processors. That broadens the instance choices available for these engines.

AWS Aurora Serverless gets faster burst scaling

AWS Aurora serverless now scales faster for bursty workloads. Amazon Aurora Serverless can now scale up in larger steps, adding up to 16 ACUs within a second and continuing up to 256 ACUs as demand grows. It still scales back to zero when idle.

AWS adds more flexibility to Apache Iceberg table management in Glue Data Catalog

AWS Data Catalog now supports table optimization, statistics, and crawlers for Apache Iceberg V3. AWS Glue Data Catalog can now optimize Apache Iceberg V3 tables, generate statistics, and discover them with crawlers. It also removes expired snapshots and orphan files.

That can improve query performance and reduce storage costs. For teams running larger data lake environments, the ability to keep tables cleaner and more discoverable can cut down on manual maintenance.

AWS S3 Tables expands quota and Iceberg support

Amazon S3 Tables now support up to 100 table buckets per AWS Region in an AWS account. Amazon S3 Tables increased the default quota to 100 table buckets per Region per account. That gives teams more room to separate datasets, workloads, or teams.

AWS S3 Tables adds full Apache Iceberg V3 data type support

Amazon S3 Tables now support all Apache Iceberg V3 data types. Amazon S3 Tables now support the full set of Apache Iceberg V3 data types, including geospatial and nanosecond timestamp types. That fills in a gap for analytics teams using richer data formats.

AWS makes Valkey cache monitoring more detailed

Amazon ElastiCache for Valkey now supports OpenTelemetry metrics and detailed monitoring. Amazon ElastiCache for Valkey now publishes OpenTelemetry metrics to CloudWatch, with a standard free monitoring mode and an optional detailed mode at higher resolution. That gives teams more visibility into cache behavior.

AWS adds partial indexes to Aurora DSQL

Amazon Aurora DSQL now supports partial indexes. Aurora DSQL can now store only qualifying rows in an index instead of indexing the whole table. That makes the index smaller and more targeted.

For workloads with a small active working set, this can improve query performance while reducing index storage costs. It’s a nice option when you want efficiency without indexing more than you need.

AWS supports Iceberg materialized views in Redshift

Amazon Redshift adds support for creating and refreshing Apache Iceberg materialized views. Amazon Redshift now supports Apache Iceberg materialized views that precompute expensive joins and aggregations, then keep results current with incremental refresh. That reduces how often the same heavy work has to run.