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Amazon GameLift Streams now supports Custom Aspect Ratio and Dynamic Resolution

Amazon GameLift Streams now supports Custom Aspect Ratio and Dynamic Resolution, giving you greater control over the streaming experience across diverse player devices and network conditions.
With Custom Aspect Ratio, you can configure a specific resolution per stream session to match your player’s device — including portrait, landscape, ultra-wide, and square aspect ratios. This eliminates letterboxing or pillarboxing, delivering native full-screen experiences on mobile phones, tablets, and non-standard displays. Specify any resolution from 320 to 4096 pixels per dimension (up to 1080p total pixel budget) using the DisplayConfiguration parameter in the StartStreamSession API. You can also try custom resolution from the AWS Console.
Dynamic Resolution automatically adapts stream quality when a player’s network bandwidth fluctuates. When bandwidth drops, the stream gracefully reduces resolution to maintain smooth playback without frame drops or disconnections — and automatically recovers to full quality when conditions improve. Dynamic Resolution is enabled by default for all new stream groups with no configuration required. Customers will need to download the new Web SDK.
Both features are available in all AWS Regions where Amazon GameLift Streams is offered. To learn more, see Custom stream resolution in the Amazon GameLift Streams Developer Guide.    https://docs.aws.amazon.com/gameliftstreams/latest/developerguide/custom-stream-resolution.html
Quelle: aws.amazon.com

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AWS Glue Data Quality now supports distribution statistics for data profiling

AWS Glue Data Quality now supports a new Distribution Analyzer that generates frequency distribution profiles for your data. Using this new Distribution Analyzer in the Data Quality Definition Language (DQDL), you can generate histograms for numeric columns and value distributions for categorical, date, and boolean columns. With support for custom bin counts, you can explore the shape and patterns of your data at the granularity that matters most to your use case. Understanding how data is distributed is foundational to building reliable data pipelines. Distribution statistics help you quickly identify skewness, outliers, and unexpected patterns across your datasets, without writing custom code. The capability integrates directly with your existing DQDL rulesets, so you can add distribution profiling alongside your current data quality checks in a single evaluation run. Distribution statistics are stored in Amazon S3 for future querying through services like Amazon Athena, and are also surfaced through APIs, making it easy to integrate distribution insights into monitoring workflows and visualization tools, including SageMaker Unified Studio. AWS Glue Data Quality distribution statistics are available in all AWS commercial regions and AWS GovCloud (US) regions. To learn more about Glue Data Quality, visit the AWS Glue Data Quality documentation. To get started with using Distribution Analyzer, visit the Analyzers documentation.
Quelle: aws.amazon.com

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AWS Glue Data Quality now supports anomaly detection and writing results to the AWS Glue Data Catalog

AWS Glue Data Quality now supports anomaly detection for Catalog-based data quality evaluations and the ability to write evaluation results to AWS Glue Data Catalog (GDC) tables. These capabilities work across both ETL jobs and Catalog evaluations, giving you a consistent data quality experience regardless of workflow type. With anomaly detection support for GDC, you can identify unexpected changes in data statistics such as sudden drops in distinct values or row count spikes in your GDC tables using ML-powered time-series forecasting, without writing rules with explicit thresholds. This is especially valuable for data engineers monitoring hundreds of tables in the GDC who need to surface issues automatically. With results storage in GDC, Data Quality rule outcomes, profiling metrics, and anomaly predictions (with confidence bounds) are written back to GDC tables, creating a queryable record of all quality evaluations. Whether the evaluation runs in an ETL job or directly on a Catalog table, results can be queried at any time using standard SQL. AWS Glue Data Quality anomaly detection and Catalog results storage are available in all AWS commercial regions and AWS GovCloud (US) regions. To get started, visit the AWS Glue Data Quality documentation.
Quelle: aws.amazon.com

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Amazon Neptune now supports tag-based access control for IAM

Amazon Neptune Database now supports tag-based access control (TBAC) for IAM, enabling customers to use AWS resource tags and IAM principal tags as conditions in IAM policies and Service Control Policies (SCPs) to control access to Neptune data-plane operations. Neptune already provides robust security through VPC isolation, TLS encryption, and IAM authentication, but customers managing multiple clusters at scale needed a dynamic, attribute-based mechanism to enforce organizational access boundaries. TBAC addresses this by allowing administrators to govern cluster access without enumerating specific cluster ARNs in every policy. With TBAC, IAM principals can only perform `neptune-db:*` actions against Neptune clusters whose tags match their own — for example, a principal tagged `Project=FraudDetection` is automatically restricted to clusters sharing that same tag. This eliminates lateral access risk within shared VPC environments, enforces team and environment-level isolation across projects, and supports federated identity workflows using SAML or OIDC session tags from external identity providers. Granular permissions like neptune-db:QueryLanguage can be used alongside TBAC for more fine-grained access control. This feature is available in all AWS Regions where Amazon Neptune is available and requires Neptune engine version 1.2.0.0 or later with IAM authentication enabled. To learn more about configuring tag-based access control for Amazon Neptune, including how to tag your DB clusters and IAM principals and deploy organization-wide guardrails using SCPs, visit the Amazon Neptune documentation.
Quelle: aws.amazon.com