AWS Database Blog

This post discusses why DML auditing is important for some organizations, and walks you through setting it up for Amazon Keyspaces. Then, using an example, we show how native integration between Amazon Keyspaces and CloudTrail makes it straightforward to record and analyze audit trails (change events) from multiple tables in a keyspace without the use of additional tools.

Palo Alto Network’s Prisma Cloud is a leading cloud security platform protecting enterprise cloud adoption from code to cloud workflows. Palo Alto Networks chose Amazon Neptune Database and Amazon OpenSearch Service as the core services to power its Infinity Graph. In this post, we discuss the scale Palo Alto Networks requires from these core services and how we were able to design a solution to meet these needs. We focus on the Neptune design decisions and benefits, and explain how OpenSearch Service fits into the design without diving into implementation details.

Customers migrating Oracle databases to Amazon RDS for PostgreSQL or Amazon Aurora PostgreSQL might encounter the challenge of scheduling jobs that require precise sub-minute scheduling to avoid workflow disruptions and maintain business operations. In this post, we demonstrate how you can use Trusted Language Extensions (TLEs) for PostgreSQL to install and use pg_dbms_job on Amazon Aurora and Amazon RDS. pg_dbms_jobs allows you to manage scheduled sub-minute jobs.

There are numerous publicly available Resource Description Framework (RDF) datasets that cover a wide range of fields, including geography, life sciences, cultural heritage, and government data. Many of these public datasets can be linked together by loading them into an RDF-compatible database. In this post, we demonstrate how to build knowledge graphs with RDF linked data and openCypher using Amazon Neptune Analytics.

A primary reason companies move their workloads to AWS is because of cost. With AWS, cloud migration and application modernization plans are based on your business needs and not agreements or licensing. You can acquire technology on an as-needed basis, only paying for the resources you use. You can build modern, scalable applications on AWS […]

In this post, we explore approaches that address operating Amazon Neptune in a multi-tenant SaaS environment, as well as the considerations that may influence how and when to apply these strategies depending on your tenant needs.

Some customers use Oracle UTL_HTTP package to write PL/SQL programs that communicate with web (HTTP) servers and invoke third-party APIs. When migrating to Amazon Aurora PostgreSQL-Compatible Edition or Amazon Relational Database Service (Amazon RDS) for PostgreSQL, these customers need to perform a custom conversion of their SQL code since PostgreSQL does not offer a similar […]

In this post, we delve into the best practices for migrating database objects from IBM Db2 z/OS to RDS for Db2 and walk you through how to validate these migrated database objects.

In this post, we present the concepts needed to use a persistent semantic cache in MemoryDB with Knowledge Bases for Amazon Bedrock, and the steps to create a chatbot application that uses the cache. We use MemoryDB as the caching layer for this use case because it delivers the fastest vector search performance at the highest recall rates among popular vector databases on AWS. We use Knowledge Bases for Amazon Bedrock as a vector database because it implements and maintains the RAG functionality for our application without the need of writing additional code.

Amazon Neptune Analytics now supports openCypher queries over RDF graphs. When you build an application that uses a graph database such as Amazon Neptune, you’re typically faced with a technology choice at the start: There are two different types of graphs, Resource Description Framework (RDF) graphs and labeled property graphs (LPGs), and your choice of […]