AWS Database Blog

In this post, we discuss the fully managed approach using Amazon Bedrock Knowledge Bases to simplify the integration of the data source with your generative AI application using Aurora. Amazon Bedrock is a fully managed service that makes foundation models (FMs) from leading AI startups and Amazon available through an API, so you can choose from a wide range of FMs to find the model that is best suited for your use case.

In this post, we explore the process of building a multi-tenant generative AI application using Aurora PostgreSQL-Compatible for vector storage. In Part 1 (this post), we present a self-managed approach to building the vector search with Aurora. In Part 2, we present a fully managed approach using Amazon Bedrock Knowledge Bases to simplify the integration of the data sources, the Aurora vector store, and your generative AI application.

Oracle Multitenant feature is available in Oracle database from 12cR1 (12.1.0.1) and later. This enables customers to use multiple PDBs in a single Oracle database, facilitating better manageability and consolidation of environments. In Oracle Multitenant architecture, there are various user management approaches available that can be used to create and manage user accounts in the container database (CDB) and PDBs. In this post we discuss the options for managing users and how they can be set up and used for different scenarios.

GaadiBazaar draws on over 25 years of vehicle finance expertise from Cholamandalam to connect vehicle buyers and sellers. Their mission is to enable hassle-free transactions at fair prices through buyer-seller interactions and end-to-end financial assistance. This post shows you how GaadiBazaar, an online platform for buying and selling vehicles, achieved significant database cost savings by migrating to Amazon Aurora MySQL Compatible Edition Serverless.

In this post we demonstrate how to enforce client-side timestamp-based write sequence order on Amazon DynamoDB. The goal is to ensure items with lower timestamps don’t overwrite items with higher timestamps, even if the requests are received out of order by the database.

In this post, we look into Kerberos authentication for Amazon Aurora PostgreSQL-Compatible Edition using AWS Directory Service for Microsoft Active Directory, and particularly the new pg_ad_mapping extension and how it can help you manage access control more efficiently.

In this post, we explore how you can achieve a patient 360-degree view using Amazon Neptune and generative AI, and use it to strengthen your organization’s research and breakthroughs. By consolidating information from multiple sources such as electronic health records (EHRs), lab reports, prescriptions, and medical histories into a single location, healthcare providers can gain a better understanding of a patient’s health.

2024 marked a significant year for Amazon DynamoDB, with advancements in security, performance, cost-effectiveness, and integration capabilities. This year-in-review post highlights key developments that have enhanced the DynamoDB experience for our customers. Whether you’re a long-time DynamoDB user or just getting started, this post will guide you through the most impactful changes of 2024 and how they can help you build reliable, faster, and more secure applications. We’ve sorted the post by alphabetical feature areas, listing releases in reverse chronological order.

In this post, we walk you through a solution to aggregate RDS orphan snapshots across accounts and AWS Regions, enabling automation and organization-wide visibility to optimize cloud spend based on data-driven insights. Cross-region copied snapshots, Aurora cluster copied snapshots and shared snapshots are out of scope for this solution. The solution uses AWS Step Functions orchestration together with AWS Lambda functions to generate orphan snapshot metadata across your organization. Generated metadata information is stored in Amazon Simple Storage Service (Amazon S3) and transformed into an Amazon Athena table by AWS Glue. Amazon QuickSight uses the Athena table to generate orphan snapshot insights.

Aqua Security is the pioneer in securing containerized cloud native applications from development to production. Like many organizations, Aqua faced the challenge of efficiently exporting and analyzing large volumes of data to meet their business requirements. Specifically, Aqua needed to export and query data at scale to share with their customers for continuous monitoring and security analysis. In this post, we explore how Aqua addressed this challenge by using aws_s3.query_export_to_s3 function with their Amazon Aurora PostgreSQL-Compatible Edition and AWS Step Functions to streamline their query output export process, enabling scalable and cost-effective data analysis.