Category: Artificial Intelligence

Oracle Database 26ai is now available on Amazon RDS for Oracle, bringing generative AI, vector search, and machine learning into the engine. In this post, part 1 of a three-part series, learn how to set up Select AI to run natural language queries against your relational data using foundation models on Amazon Bedrock, all from within a private Amazon RDS instance.

In this post, we demonstrate a serverless approach to continuous forensic artifact collection for Amazon RDS and Amazon Aurora databases. By capturing point-in-time snapshots of database internals on a cadence and storing them in Amazon S3, you create a time-series record that AI tools can analyze in seconds. This turns what was hours of manual investigation into an instant conversation.

In this post, we demonstrate how SQL Server 2025 on Amazon RDS can call Amazon Bedrock foundation models directly from T-SQL using sp_invoke_external_rest_endpoint. This approach removes middleware, reduces latency, and brings AI capabilities directly into database workflows.

In this post, we show you how to build a semantic ontology that helps your AI assistants navigate enterprise data efficiently. You’ll learn how to structure a property graph store for data relationships, set up vector indexing for semantic search, and implement an automated fact-learning layer that improves use. This bottom-up approach grounds your ontology in the data that exists, building abstractions from observed patterns rather than theoretical models.

In this post, we demonstrate an approach to review RDS logs using Kiro, an AI-powered conversational assistant combined with the Model Context Protocol (MCP) server from awslabs.cloudwatch-mcp-server. This solution transforms log analysis from a technical, query-based process into a natural language conversation, delivering actionable insights instantly.

Running pgvector on Amazon Aurora PostgreSQL gives you a production-grade vector store on a database you already know, backed by the operational tooling, high availability, and scaling behaviour of Amazon Aurora. Production traffic does introduce a predictable set of operational considerations: query latency as the corpus grows, recall on filtered vector searches, memory headroom during index builds, and connection behaviour under load. This post is scoped to the database operations that keep the RAG retrieval layer healthy. In this post, we cover the operational practices that keep a pgvector workload healthy once you depend on it: choosing the right index and distance function, scaling with quantization and partitioning, managing Hierarchical Navigable Small World (HNSW) churn, sizing for memory-resident operation, and the observability signals that catch problems early.

In this post, you learn how to build a generative AI–powered migration assistant that helps automate portions of the last mile of code conversion. Using Anthropic’s Claude Sonnet 4.6 on Amazon Bedrock, the Strands Agents framework, and the AWS Knowledge MCP Server, you can automate the conversion and validation of PL/SQL objects against Amazon Aurora PostgreSQL-Compatible Edition. The assistant reads the AWS DMS SC assessment CSV, fetches live PL/SQL source from Oracle, converts each object, deploys the result to Aurora PostgreSQL through AWS Lambda, and runs automated tests, in a single pipeline.

Today, we announce the Amazon Aurora MySQL power for Kiro. The power connects Kiro’s AI agent to Aurora MySQL and pairs live database access with curated best-practice guidance. You describe what you need in natural language. The agent generates the API calls, SQL, and configuration for you to review and run. In this post, we walk through how the power guides a production migration from Amazon Relational Database Service (Amazon RDS) for MySQL 8.0 to Aurora MySQL through four phases: assessment, replica creation, promotion, and post-cutover validation.

Amazon receives millions of visits every day, and earning each customer’s trust visit after visit is the foundation that the store is built on. A meaningful part of that trust comes down to whether the recommendations we surface feel relevant and whether they reflect what the customer actually cares about in the moment. In this post, we describe an architecture that makes it achievable. Amazon SageMaker hosts a sentence transformer model on a managed endpoint and turns customer query text into dense semantic vectors. Valkey is an open source, in-memory data store with built-in vector search. It’s available on AWS through Amazon ElastiCache and Amazon MemoryDB. In our architecture, we use Amazon-managed Valkey to store the product catalog as a vector index.

In this post, we show how to build an Amazon Aurora DSQL database agent that other AI agents can discover and query through natural language using the A2A protocol. You’ll walk through how to build and deploy this using Amazon Bedrock AgentCore capabilities, including AgentCore Runtime for hosting, AgentCore Gateway for tool access via MCP, and the Strands Agents SDK for agent logic.