AWS Database Blog
Category: Technical How-to
Navigating backup and recovery options for Oracle Database@AWS
Oracle Database@AWS (ODB@AWS) delivers Oracle Exadata infrastructure, managed by Oracle Cloud Infrastructure (OCI), directly within Amazon Web Services (AWS) data centers. In this post, we walk you through the backup and recovery options available for ODB@AWS services: Oracle Exadata Database Service on Dedicated Infrastructure (ExaDB-D) and Oracle Autonomous AI Database on Dedicated Exadata Infrastructure (ADB-D).
Optimize full-text search in Amazon RDS for MySQL and Amazon Aurora MySQL
In this post, we show you how to optimize full-text search (FTS) performance in Amazon RDS for MySQL and Amazon Aurora MySQL-Compatible Edition through proper maintenance and monitoring. We discuss why FTS indexes require regular maintenance, common issues that can arise, and best practices for keeping your FTS-enabled databases running smoothly.
Working with identity columns and sequences in Aurora DSQL
Amazon Aurora DSQL now supports PostgreSQL-compatible identity columns and sequence objects, so developers can generate unique integer identifiers with configurable performance characteristics optimized for distributed workloads. In distributed database environments, generating unique, sequential identifiers is a fundamental challenge: coordinating across multiple nodes creates performance bottlenecks, especially under high concurrency workloads. In this post, we show you how to create and manage identity columns for auto-incrementing IDs, selecting between identity columns and standalone sequence objects, and improving cache settings while choosing between UUIDs and integer sequences for your workload requirements.
Stream live data from Amazon Keyspaces to S3 vector for real time AI applications
In this post, you learn how to build a real-time AI movie recommendation system by streaming live data changes from Amazon Keyspaces to Amazon S3 vector storage. The post shows how to use Keyspaces change data capture streams to capture database modifications, convert them into vector embeddings using Amazon Bedrock, and store them in S3 Vector indexes for similarity searches that give AI applications access to fresh data within milliseconds.
Turbocharge your applications with Amazon DocumentDB 8.0
Amazon DocumentDB 8.0 brings in support for MongoDB 8.0 API driver compatibility while maintaining support for applications built using MongoDB API versions 6.0 and 7.0. This post explores the new features in Amazon DocumentDB 8.0 and demonstrates how they improve performance and cost efficiency.
Conversational Oracle EBS operations with CloudWatch MCP and Kiro CLI
In this post, you learn how to implement conversational operations for Oracle E-Business Suite (Oracle EBS) on AWS by connecting Kiro CLI with your monitoring infrastructure through the MCP. We walk through the technical architecture that enables natural language queries to retrieve CloudWatch metrics, analyze logs, and execute operational commands.
Augment DMS SC with Amazon Q Developer for code conversion and test case generation
You can use the AWS Database Migration Service Schema Conversion (AWS DMS SC) with generative AI feature to accelerate your database migration to AWS. This feature automatically handles the conversion of many database objects during migration by using traditional rule-based techniques and deterministic AI techniques. In this post, we demonstrate how Amazon Q Developer delivers generic solutions for complex AWS DMS SC issues, intelligently converts database stored procedure code from source to target database-compatible code, and automatically generates comprehensive test cases to validate your migrated database objects.
Enabling nested transactions in Amazon DynamoDB using C#
In this post, I introduce a framework for managing atomicity, consistency, isolation, and durability (ACID) compliant transactions in Amazon DynamoDB using C#, featuring support for nested transactions. This capability allows you to implement sophisticated logic with finer control over data consistency and error handling within your .NET applications. With this nested transaction framework, you can isolate issues, allow for partial rollbacks, and build maintainable, modular workflows on top of the built-in transactional capabilities of DynamoDB.
Automatically scale storage for Amazon RDS Multi-AZ DB clusters using AWS Lambda
In this post, we walk you through building an automated storage scaling solution for Amazon RDS Multi-AZ clusters with two readable standbys. We use AWS Lambda to execute scaling logic, Amazon CloudWatch to detect and alarm on storage thresholds, and Amazon SNS to deliver timely notifications. This combination provides event-driven automation, native AWS integration, and operational visibility without requiring third-party tooling.
Synchronizing a Backup on-premises Db2 Server with Amazon RDS for Db2
In this post, we provide guidance on implementing a hybrid architecture where a self-managed Db2 instance remains synchronized with Amazon RDS for Db2 via continuous archive log application, ensuring organizations maintain strategic deployment options without compromising the advantages of cloud-native managed services.









