News For Dev-ops

AWS Backup for Amazon S3 now supports creating S3 Access Points, providing immediate read-only access to backup data using standard S3 APIs without initiating a restore. This enables targeted file recovery, data validation, compliance auditing, and forensic investigation while your backup data remains protected in your backup vault. You can create an access point for an S3 recovery point and read backup data using standard S3 operations such as GetObject, HeadObject, and ListObjectsV2. Access points work with both snapshot and continuous (point-in-time) recovery points stored in standard backup vaults or logically air-gapped vaults, including recovery points shared across accounts through AWS Resource Access Manager or Multi-party approval. While an access point is active, the associated recovery point is protected from deletion. Get started with this capability by creating access points for your S3 recovery points using the AWS Backup console, API, or CLI. This capability is avail...

AWS Glue Data Catalog is a centralized metadata repository that helps you discover and understand data, with semantic search and support for adding business context to datasets currently in preview. Today, we are adding two new capabilities to this preview: exporting catalog metadata exports to S3 Tables for metadata querying, and expanding the semantic search preview to catalogs encrypted with AWS Key Management Service (AWS KMS) customer managed keys. When you enable the Glue metadata export, your catalog's technical and business metadata, including glossary terms, custom metadata attachments, and asset descriptions, are written to tables in the managed aws-catalog S3 table bucket. Metadata is stored in Apache Iceberg format, enabling you to query, audit, and perform time travel queries for historical analysis using standard SQL in query engines that support Iceberg, such as AWS analytics services like Amazon Athena and Amazon QuickSight as well as third-party tools. This lets ...

We're excited to announce the Azure DevOps service connection , a new way to access Azure DevOps from your pipelines using a Microsoft Entra workload identity (a service principal or managed identity) instead of a Personal Access Token (PAT) or session token. This post walks through what it is, how to set it up, and the many places you can use it, including several capabilities we've added based on your feedback. Why use an Azure DevOps service connection? Using an Azure DevOps service connection improves the security of your pipelines in several ways: PAT-free authentication: Eliminate the need to create, store, and rotate Personal Access Tokens Least privilege: Use per-pipeline or even task-level permissions instead of shared build service account permissions No persistent secrets: Use Microsoft Entra federated credentials instead of passwords Audit trail: All authentication attempts are logged in the Azure DevOps audit log Because the connection authenticate...

Last time, we tried to execute on our plan to use the global interface table to hold hold a reference to an object in another apartment that automatically expires when the apartment runs down . But it broke down because objects that are marked INoMarshal can't go into the global interface table. So we will just force the square peg into the round hole: We can put the non-marshalable object inside an object that is marshalable. template<typename Smart> struct force_marshal : winrt::implements<force_marshal<Smart>, ::IUnknown, winrt::non_agile> { force_marshal(Smart const& p) : m_p(p) {} Smart m_p; }; The force_marshal<Smart> object babysits a non-marshalable smart pointer to a COM object and exposes a marshalable wrapper around it. Since the wrapped object is not agile, the wrapper cannot be either. (If the wrapper were agile, then we'd be back where we started: How do we ensure that the m_p is destructed in the correct apartment?)...

AWS Glue Data Quality now offers improved anomaly detection with a new observation mode that reduces detection of false anomalies and removes pricing for anomaly detection in ETL jobs. Customers using notebook-based or exploratory workflows now benefit from smarter anomaly detection that gracefully handles irregular data arrival intervals. This new capability avoids over-extrapolating trends by using a constant baseline instead of a linear trend, delivering more accurate alerts and reducing noise so teams can focus on genuine anomalies. The new anomaly detection observation mode is particularly useful for exploratory data analysis, datasets with flat or random patterns, workloads without predictable trends and cases where you run data quality checks on varying schedules or in interactive environments like notebooks. Additionally, anomaly detection for AWS Glue ETL jobs is now available at no additional cost, so you can monitor data quality anomalies across all your Glue pipelines...

Amazon Relational Database Service (Amazon RDS) for SQL Server now supports publishing SQL Server Audit logs to CloudWatch. SQL Server Audit is a native SQL Server feature that allows tracking and logging events that occur on the Database Engine. On Amazon RDS, you can create audits and audit specifications in the same way that you create them for on-premises SQL Server database servers. Now you can publish the audit logs to S3, CloudWatch, or both. If you enable both S3 and CloudWatch options, the audit log publication will be marked as "completed" only after the audit log files are uploaded to both S3 and CloudWatch. Once the audit logs are in CloudWatch, you can perform real-time analysis of the log data. If you enable retention, RDS keeps your audit logs on your DB instance for the configured period of time. For more information, see SQL Server Audit (database engine) in the SQL Server documentation. For detailed configuration instructions, see the Amazon RDS for SQ...