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COMPANY · ENTITY #4005

Amazon Aurora PostgreSQL

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EVENT TIMELINE

4

COMPANIES · 1 SOURCE · AWS Machine Learning

AtScale C2026.7 walkthrough: build a semantic layer for Amazon Bedrock AgentCore agents

A technical walkthrough demonstrates using AtScale (C2026.7) as a dimensional semantic layer (modeled in SML) for agentic workflows on Amazon Bedrock AgentCore. The post shows a Strands agent running on AgentCore (with Anthropic Claude Sonnet 4.6 for the demo), how AtScale exposes a single model over SQL/XMLA/MCP, identity and RBAC propagation through AgentCore to AtScale, query pushdown to Amazon Redshift Serverless, and deployment on Amazon EKS.

5.0

COMPANIES · 1 SOURCE · InfoQ AI, ML & Data Engineering

Amazon acquires DuckLabs and integrates DuckDB into Amazon Aurora PostgreSQL

Amazon (AWS) has acquired DuckLabs, the team behind the analytical database DuckDB; co‑founders Hannes Mühleisen and Mark Raasveldt will join AWS and continue to lead the project. AWS says DuckDB and related components will remain MIT‑licensed and overseen by the DuckDB Foundation, and has already integrated DuckDB into Amazon Aurora PostgreSQL so a single query can combine live Aurora data with historical Iceberg/Parquet data in S3 (via AWS Glue) without separate ETL; the feature is available in all commercial regions and AWS GovCloud (US) with customers paying only for incremental Aurora compute and S3 requests.

8.0

CODING · 1 SOURCE · AWS Machine Learning

AWS guide: building a contract-intelligence platform with Bedrock AgentCore

An AWS how-to describes an architecture for a React web app that converts portfolios of contract PDFs into structured, queryable data: AI extraction agents running via Amazon Bedrock (AgentCore) use Claude Sonnet to extract fields and Claude Haiku to verify them, Amazon Textract acts as a deterministic tiebreaker for signatures, results are stored in Amazon Aurora PostgreSQL, and users query via embedded analytics and a natural-language chat interface with real-time pipeline status over WebSocket.

6.0

COMPANIES · 1 SOURCE · AWS Machine Learning

AWS outlines vector-store choices for Amazon Bedrock Knowledge Bases (OpenSearch, Aurora pgvector, S3 Vectors)

AWS published guidance for the customer-managed configuration of Amazon Bedrock Knowledge Bases, comparing three supported vector-store backends—Amazon OpenSearch Service (managed and serverless), Amazon Aurora PostgreSQL with pgvector, and Amazon S3 Vectors—and evaluating their tradeoffs across RAG use cases (latency, cost, scalability). The post maps each backend to example workloads such as e-commerce product search and discusses performance and cost implications.

6.0