AWS★★★★
Amazon DynamoDB now supports filtered export for tables. Export to Amazon S3 allows you to export your table data for analytics, data sharing, and other offline uses, as either a full export or an incremental export over a time window. Filtered export enables you to specify exactly which items and attributes to export, producing a dataset that contains only the data relevant to your use case. With filtered export, you use a key condition expression on a key attribute and a filter expression on any attribute to select which items to export, and a projection expression to choose which attributes to include. The export then returns only the ite…
SECURITY★★★★
On September 30, 2026, Amazon announced a patch update for the following Amazon Corretto Long-Term Support (LTS) version of OpenJDK: Corretto 8u504 is now available for download. Amazon Corretto is a no-cost, multi-platform, production-ready distribution of OpenJDK. This patch includes the tzdata 2026d updates. Visit Corretto home page to download Corretto 27, Corretto 25, Corretto 21, Corretto 17, Corretto 11, or Corretto 8. You can also get the updates on your Linux system by configuring a Corretto Apt, Yum, or Apk repo. Feedback is welcomed!
AWS★★★★
Amazon S3 Object Lock support for variable retention with event holds is now available in AWS GovCloud (US-East) and AWS GovCloud (US-West). Amazon S3 Object Lock variable retention allows you to apply write-once-read-many (WORM) protection to objects whose required retention period starts with a future event, such as a contract closing or an audit completing. You place an event hold with a retention duration on an object and S3 protects the object while the hold is in place. When you release the hold, S3 retains the object for the duration you specified. Unlike legal holds, which end protection immediately upon removal, event holds provide …
AWS★★★★
AWS Glue Data Catalog now supports table optimization, statistics, and crawlers for Apache Iceberg Version 3 (V3) tables. With these new capabilities, you can automatically maintain V3 tables, optimize them for query performance, and discover them in Amazon S3. With table optimization, you can compact V3 tables using binpack, sort, or z-order strategies to improve query performance, and remove expired snapshots and orphan files to reduce storage costs. These optimizations support V3 data types, including variant, geospatial, and nanosecond-precision timestamps. You can also generate number of distinct values (NDV) statistics for V3 tables, w…
AWS★★★★
Amazon S3 Tables now support up to 100 table buckets per AWS Region in an AWS account, increased from 10. This allows you to create up to 1 million tables per AWS Region in an AWS account. With a higher table bucket allowance, you can create a separate table bucket for each dataset, workload, or team, and apply table bucket-level settings such as encryption, access policies, and replication to each. The higher quota applies by default to all accounts at no additional cost. S3 Tables deliver the first cloud object store with built-in Apache Iceberg support, and the easiest way to store tabular data at scale. S3 Tables perform continual table …
AWS★★★★
Today, AWS announces system-managed materialized views for Apache Iceberg. Materialized views let you precompute an expensive query once and reuse the result across engines. Because this result is an open Iceberg table in your data lake, anyone with write access can change it, either through the catalog or by writing to the files in Amazon S3. System-managed materialized views close this gap by only allowing AWS Glue to write the materialized view's data and definition, so the result stays exactly as computed regardless of who has write access to the table or the underlying S3 files. To get started, write the SQL that defines the materialize…
AWS★★★★
Amazon S3 Tables now support all data types in the Apache Iceberg V3 spec, along with column default values, deletion vectors, and row lineage. Upgrade existing V2 tables or create new V3 tables with built-in compaction, maintenance, and engine compatibility.
AWS★★★★
Today, AWS announces system-managed materialized views for Apache Iceberg. Materialized views let you precompute an expensive query once and reuse the result across engines. Because this result is an open Iceberg table in your data lake, anyone with write access can change it, either through the catalog or by writing to the files in Amazon S3. System-managed materialized views close this gap by only allowing AWS Glue to write the materialized view's data and definition, so the result stays exactly as computed regardless of who has write access to the table or the underlying S3 files. To get started, write the SQL that defines the materialize…
AWS★★★★
Amazon S3 Vectors now supports metadata pre filtering, delivering up to 5x higher recall on filtered searches. Filters evaluate before similarity search so scoped queries return more relevant results. Ideal for RAG, agentic apps, and document search.
AWS★★★★
Amazon S3 Vectors now supports pre-filtering, which evaluates metadata filters before running similarity search, returning up to 5x more of the matching vectors when your filter is selective. S3 Vectors also adds a prefix match operator ($startsWith) for filtering on values like paths and URLs. Together, these improvements give your retrieval-augmented generation (RAG), agentic, and semantic-search applications more complete results when you filter, so your applications return more relevant answers. S3 Vectors provides native support to store and query vectors in Amazon S3, delivering purpose-built, cost-optimized vector storage and query at…
AWS★★★★
Amazon Managed Grafana now supports creating new workspaces with Grafana version 13.2. This release brings features from open-source Grafana versions 13.0 to 13.2, including Git Sync for dashboards, dynamic dashboards, and PromQL query support in the Amazon CloudWatch data source plugin. With Git Sync, you can treat dashboards as code by linking a workspace to a Git repository, enabling version control for tracking, reviewing, and reverting dashboard modifications. Dynamic dashboards provide a more adaptable approach to layout and paneling, constructing responsive views driven by conditions that respond to data and variables. The Amazon Clou…
AWS★★★★
AWS Parallel Computing Service (AWS PCS) now supports scaling logs, which record how AWS PCS scales the compute node groups in your cluster. Each log entry records one state transition for one compute node—for example, an instance launch, a node registration, a scale-down, or a launch failure with its reason. Using scaling logs, you can more easily troubleshoot scaling issues. For example, you can determine why a compute node group did not reach its target size, which launches failed for capacity reasons, and when a specific node started or stopped. Scaling log delivery is opt-in, and you can configure AWS PCS to emit scaling logs to Amazon …
