AWS Database Blog

Healthcare providers manage an ever-growing volume of patient data and medication information to help ensure safe, effective treatment. Although traditional database systems excel at storing patient records, they require complex queries to access information. By adding generative AI capabilities, healthcare providers can now use natural language to search patient records and verify medication safety, rather than writing complex database queries. In this post, I show you a solution that uses HAQM Bedrock and HAQM DynamoDB to create an AI agent that helps healthcare providers quickly identify potential drug interactions by validating new prescriptions against a patient’s current medication records.

As companies expand globally, they must be able to architect highly available and fault-tolerant systems across multiple AWS Regions. With such scale, a company can find itself in this position when designing a caching solution across its multi-Region infrastructure. In this post, we dive deep into how to use HAQM ElastiCache for Valkey, a fully managed in-memory data store with Redis OSS and Valkey compatibility, and the HAQM ElastiCache for Valkey Global Datastore feature set.

In this post, we guide you through setting up automation for pre-upgrade checks and upgrading a fleet of HAQM RDS for PostgreSQL instances. In this solution, we use AWS Systems Manager to automate the HAQM RDS upgrade job.

In this post, we explore how pgvector 0.8.0 on Aurora PostgreSQL-Compatible delivers up to 9x faster query processing and 100x more relevant search results, addressing key scaling challenges that enterprise AI applications face when implementing vector search at scale.

In this post, we describe some of the openCypher features that have been released as part of the 1.4.2.0 engine update to HAQM Neptune. Neptune provides developers with the choice of building their graph applications using three open graph query languages: openCypher, Apache TinkerPop Gremlin, and the World Wide Web Consortium’s (W3C) SPARQL 1.1. You can use the guide at the end of this post to try out the new features that are described.

In this post, we describe a solution to integrate generative AI applications with relational databases like HAQM Aurora PostgreSQL-Compatible Edition using RDS Data API (Data API) for simplified database interactions, HAQM Bedrock for AI model access, HAQM Bedrock Agents for task automation and HAQM Bedrock Knowledge Bases for context information retrieval.

In this post, we show you essential post-migration tasks to perform after migrating your SQL Server database to HAQM EC2 and how to automate this activity by using Cloud Migration Factory on AWS (CMF), such as validating database status, configuring performance settings, and running consistency checks. Additionally, we explore how the CMF solution can automate these essential tasks, providing efficiency, scalability, and heightened visibility to simplify and expedite your migration process.

In this post, we dive deep into HAQM Aurora Global Database’s new support for up to 10 secondary Regions and explore use cases it unlocks. An Aurora Global Database consists of one primary Region and up to 10 read-only secondary Regions for low-latency local reads.

In this post, we demonstrate how to configure a linked server between HAQM RDS for SQL Server and a Teradata database instance. We guide you through the step-by-step process to establish this connection and show you how to verify its functionality.

In this post, we demonstrate how upgrading to Graviton4-based R8g instances with Aurora PostgreSQL-Compatible 17.4 on Aurora I/O-Optimized cluster configuration can deliver significant price-performance gains – delivering up to 1.7 times higher write throughput, 1.38 times better price-performance and reducing commit latency by up to 46% on r8g.16xlarge instances and 38% on r8g.2xlarge instances as compared to Graviton2-based R6g instances.