Category: Technical How-to

In this post, we walk through how we used Kiro, an agentic Integrated Development Environment (IDE), to build a complete fitness center management application that digitizes paper-based fitness tracking. We explore Kiro’s spec-driven development workflow and see how it transforms complex application development into a streamlined, iterative process. Our solution uses Amazon DocumentDB as the backend.

Amazon RDS for SQL Server now supports the Optimize CPU feature. With the Optimize CPU feature you can define the number of vCPUs when you launch new instances or when modifying existing database instances. This feature also provides a detailed billing breakdown of RDS infrastructure costs, and licensing costs for SQL Server and Windows OS. It is available starting from the 7th Generation instance class. In this post, we explore how to use the Optimize CPU feature with Amazon RDS for SQL Server.

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.