AWS

AWS has extended its Transform tool for full-stack Windows modernization to support deploying converted Aurora PostgreSQL databases across regions in seven additional AWS Regions. Users can now upload SQL Server DDL files and explicitly set a target region for the resulting Aurora cluster during the transformation plan. The workflow continues to handle dependent .NET application updates, including connection strings and data-access calls, to ensure PostgreSQL compatibility.

• Cross-region deployment for converted Aurora PostgreSQL is now available in 7 new AWS Regions.
• Upload SQL Server DDL files and specify a target region for the modernized database cluster.
• Automated handling of .NET app updates, including connection strings and ADO.NET/EF calls.
• Improves data residency, latency, and disaster recovery options for SQL Server modernization.

AWS has extended Web Search availability on Amazon Bedrock AgentCore to US East (Ohio), US West (Oregon), Asia Pacific (Sydney), and AWS GovCloud (US-West). This fully managed tool allows AI agents to ground their responses in current web data for improved accuracy in research and summarization tasks. The expansion enables lower latency by processing data closer to end users and supports government workloads in meeting specific compliance and data residency mandates.

• Web Search is now live in US East (Ohio), US West (Oregon), Asia Pacific (Sydney), and GovCloud (US-West).
• Agents gain access to real-time web grounding, enhancing accuracy for QA and summarization workflows.
• Reduced latency is achieved by retrieving information closer to the application and user location.
• GovCloud availability ensures compliance for government workloads with strict data residency requirements.

Amazon DynamoDB Streams now accepts a specific timestamp to start reading records, moving beyond the previous limitation of starting only from the oldest retained entry or new writes. This new AT_TIMESTAMP shard iterator type allows consumers to jump directly to a point in time, eliminating the need to process large volumes of historical data to reach a specific offset. The feature integrates with the Amazon Kinesis Client Library via the DynamoDB Streams Kinesis adapter for seamless implementation.

• Resume stream consumption from an exact point in time rather than scanning from the beginning of the retention window.
• Use AT_TIMESTAMP shard iterator to skip irrelevant historical records during recovery or debugging scenarios.
• Integrates with Kinesis Client Library (KCL) using the DynamoDB Streams Kinesis adapter.
• Reduces latency and cost when replaying data for auditing or error recovery within the 24-hour retention period.