Cloud Computing Köln

GLM 5.3 by Z.ai is now generally available on Amazon Bedrock

Amazon Bedrock now supports GLM 5.3 from Z.ai, giving you a powerful new option for agentic coding and long-horizon software engineering work. Customers using GLM 5.3 on Amazon Bedrock have access to the latest open weight model with the AWS security and compliance posture.
GLM 5.3 is Z.ai’s flagship model, a mixture-of-experts architecture with 753B total parameters and roughly 40B active per token, built on the same base model as GLM 5.2 with all improvements driven by scaled post-training. It combines a 1-million-token context window with up to 128K output tokens, and reasoning is always enabled with selectable effort levels so you can trade latency and token consumption against task performance.
Bedrock support for GLM 5.3 includes explicit prompt caching with cache points on system prompts and messages, helping you reduce latency and input costs when reusing context across model calls.
GLM 5.3 is available to eligible enterprise customers, and is accessible through the US and Global cross-Region inference profiles. To get started with GLM 5.3, you can access the model in the Amazon Bedrock console or programmatically through supported Amazon Bedrock APIs. For information about supported AWS Regions, endpoints, APIs, features, inference profiles and pricing, see the Amazon Bedrock documentation. To learn more, read the launch blog post.
Quelle: aws.amazon.com

Amazon Redshift adds support for creating and refreshing Apache Iceberg materialized views

Amazon Redshift now supports the creation and refresh of Apache Iceberg materialized views. Materialized views pre-compute expensive joins and aggregations once and store the results in an Apache Iceberg table in Amazon S3 or Amazon S3 table buckets, registered in the AWS Glue Data Catalog. Materialized views are created using familiar SQL — CREATE MATERIALIZED VIEW … USING ICEBERG and the results are instantly queryable by any Iceberg-compatible engine, including Amazon Athena, Apache Spark on Amazon EMR and AWS Glue, and third-party engines such as Trino, or Snowflake. Redshift keeps them current by recomputing only what has changed with manual incremental refresh, and because the results are Iceberg tables in the Glue Data Catalog, they are governed and discovered like any other catalog table.
Data teams often build analytics in stages, stitching together different engines to clean and transform raw data before serving it. This adds pipeline orchestration overhead and can introduce semantic differences between engines. Iceberg materialized views deliver value in two ways. First, instead of hundreds of users and teams re-running the same expensive joins and aggregations, and re-scanning source tables on every query, you compute the result once and everyone reads the precomputed table. Second, you can run an end-to-end pipeline using a single engine like Apache Spark and now Amazon Redshift, and every downstream consumer shares the same open result without the overhead of orchestrating multiple engines. Either way, the output is an open Iceberg table that any engine can read without copies or conversion. You can still load these tables into Redshift Managed Storage (RMS) as native RMS materialized views for your most performance-sensitive dashboards.
You can create Iceberg Materialized Views in any region where Redshift Serverless and provisioned Graviton instances are supported. To learn more, see Materialized views stored as Apache Iceberg tables in the Amazon Redshift Database Developer Guide, the CREATE MATERIALIZED VIEW command reference, and the Materialize once, query anywhere blog post.
Quelle: aws.amazon.com

AWS Private CA now provides detailed certificate issuance logs

AWS Private CA announces detailed certificate issuance logs, a new AWS CloudTrail service event that records the complete certificate content, issuing CA information, requester identity, and signing status for every issuance. You can use these events for compliance auditing, certificate inventory, algorithm migration tracking, and failed-issuance monitoring. Previously, the CloudTrail management event for the IssueCertificate API confirmed that the API call succeeded by providing a certificate ARN but did not capture the certificate content, information about the CA that signed it, or issuances that failed before signing.
The new IssueCertificateDetails event captures the complete to-be-signed (TBS) certificate with all X.509 fields and extensions, plus convenience fields for the subject, issuer, serial number, validity period, template, and signing algorithm. Events are emitted for both successful and failed issuance, so pre-signing failures such as name constraints violations now produce a record with a failure explanation. Each event identifies the requester: the account and IAM principal for direct API callers, or the service principal for certificates issued through AWS Private CA connectors or integrated AWS services. In cross-account configurations, the event is delivered to the CA owner account.
The event is delivered automatically as a CloudTrail management event, with no configuration or opt-in required and no additional cost beyond standard AWS CloudTrail pricing. Because it flows through CloudTrail, you can act on it in real time with Amazon EventBridge or query it in batch with Amazon Athena for certificate auditing, inventory, tracking, and monitoring. This feature is available in all AWS Regions where AWS Private CA is offered. To learn more, see the AWS Private CA User Guide.
Quelle: aws.amazon.com

AWS Advanced Ruby Driver Wrapper is generally available

The Amazon Web Services (AWS) Advanced Ruby Driver Wrapper is now generally available for use with Amazon RDS and Amazon Aurora PostgreSQL and MySQL-compatible databases. This advanced database driver reduces RDS Blue/Green switchover, Aurora Global database switchover and database failover times, improving application availability. Additionally, it supports multiple authentication mechanisms for your database, including AWS Secrets Manager authentication, and token-based authentication with AWS Identity and Access Management (IAM).
The AWS Advanced Ruby Driver Wrapper builds on top of the community pg (PostgreSQL), and the mysql2 (MySQL) drivers to provide enhanced functionality beyond standard database connectivity. The wrapper is natively integrated with Aurora and RDS databases, enabling it to monitor database cluster status and quickly connect to newly promoted writers during unexpected failures that trigger database failovers. Furthermore, the wrapper seamlessly integrates with the ActiveRecord through provided aws_postgresql and aws_mysql2 adapters, so you can enable it without changing your application code.
The driver is available as an open-source project under the Apache 2.0 license. Refer to the instructions on the GitHub repository to get started.
Quelle: aws.amazon.com