AWS Database Blog

Category: Technical How-to

Build graph applications faster with Amazon Neptune public endpoints

Developing applications on Amazon Neptune Database historically required users setup access into the VPC where it is hosted and use either 3rd party drivers or direct HTTP requests. In this post, we discuss how two key features, public endpoints and the Neptune Data API, solve these common challenges in Amazon Neptune application development. Public endpoints […]

Implement network connectivity patterns for Oracle Database@AWS

Oracle Database@AWS (ODB@AWS) is an offering you can use to access Oracle Exadata infrastructure managed by Oracle Cloud Infrastructure (OCI) within Amazon Web Services (AWS) data centers. You can use ODB@AWS to migrate your Oracle Exadata workloads to AWS while maintaining the same performance and features as your on-premises Oracle Exadata deployments. You benefit from […]

Automating vector embedding generation in Amazon Aurora PostgreSQL with Amazon Bedrock

In this post, we explore several approaches for automating the generation of vector embedding in Amazon Aurora PostgreSQL-Compatible Edition when data is inserted or modified in the database. Each approach offers different trade-offs in terms of complexity, latency, reliability, and scalability, allowing you to choose the best fit for your specific application needs.

Group database tables under AWS Database Migration Service tasks for PostgreSQL source engine

AWS DMS accommodates a broad range of source and target data repositories, such as relational databases, data warehouses, and NoSQL databases. Proper preparation and design are vital for a successful migration process, especially when it comes to optimizing performance and addressing potential delay issues. In this blog post, we offer guidance about recognizing potential root causes of complete load and CDC delays early in the process and provide suggestions for optimally clustering tables to achieve the best performance for an AWS DMS task.