Category: Technical How-to

This post shows how to build a semantic cache using vector search on Amazon ElastiCache for Valkey. As detailed in the Impact section of this post, our experiments with semantic caching reduced LLM inference cost by up to 86 percent and improved average end-to-end latency for queries by up to 88 percent.

In this post, we demonstrate how to set up a zero-ETL integration between self-managed databases such as MySQL, PostgreSQL, SQL Server, and Oracle to Amazon Redshift. The transactional data from the source gets replicated in near real time on the destination, which processes analytical queries.

In this post, I explore how Amazon Aurora DSQL uses Amazon Time Sync Service to build a hybrid logical clock solution.

Amazon Aurora DSQL employs an active-active distributed database design, wherein all database resources are peers and serve both write and read traffic within a Region and across Regions. This design facilitates synchronous data replication and automated zero data loss failover for single and multi-Region Aurora DSQL clusters. In this post, I discuss the individual components and the responsibilities of a multi-Region distributed database to provide an ACID-compliant, strongly consistent relational database.

In this third post of the series, I examine the end-to-end processing of the two transaction types in Aurora DSQL: read-only and read-write. Amazon Aurora DSQL doesn’t have write-only transactions, since it’s imperative to verify the table schema or ensure the uniqueness of primary keys on each change – which results them being read-write transactions as well.

In this second post, I examine Aurora DSQL’s architecture and explain how its design decisions impact functionality—such as optimistic locking and PostgreSQL feature support—so you can assess compatibility with your applications. I provide a comprehensive overview of the underlying architecture, which is fully abstracted from the user.

In this post, I dive deep into fundamental concepts that are important to comprehend the benefits of Aurora DSQL, its feature set, and its underlying components.

Today, we are launching dynamic data masking feature for Amazon Aurora PostgreSQL-Compatible Edition. In this post we show how dynamic data masking can help you meet data privacy requirements. We discuss how this feature is implemented and demonstrate how it works with PostgreSQL role hierarchy.

You can now use resource governor with Amazon RDS for SQL Server Enterprise Edition to optimize your database performance by controlling how compute resources are allocated across different workloads. This post shows you how to optimize your database performance using resource governor on Amazon RDS for SQL Server. We walk you through the step-by-step process of enabling and configuring the feature, including how to set up resource pools, create workload groups, and implement classifier functions for effective resource management.

In this post, we walk through the steps of integrating Oracle Database@AWS (ODB@AWS) with Amazon Bedrock for by creating a RAG assistant application using an Amazon Titan embedding model in Amazon Bedrock and vectors stored in Oracle AI Database 26ai.