AWS Database Blog

In this post, we demonstrate how you can implement a comprehensive monitoring strategy for Amazon RDS for SQL Server by combining AWS native tools with SQL Server diagnostic utilities. We explore AWS services including AWS Trusted Advisor, Amazon CloudWatch Database Insights, Enhanced Monitoring, and Amazon RDS events, alongside native SQL Server tools such as Query Store, Dynamic Management Views (DMVs), and Extended Events. By implementing these monitoring capabilities, you can identify potential bottlenecks before they impact your applications, optimize resource utilization, and maintain consistent database performance as your business scales.

In this post, we demonstrate how to efficiently migrate relational-style data from NoSQL to Aurora DSQL, using Kiro CLI as our generative AI tool to optimize schema design and streamline the migration process.

In this post, I show you how to use the new AWS DMS instance estimator tool for initial sizing recommendations, review monitoring strategies to collect accurate benchmark data, and present considerations on how to optimize your database migrations.

In this post, we discuss configuring AWS DMS tasks to migrate HierarchyID columns from SQL Server to Aurora PostgreSQL-Compatible efficiently.

In this post, we explore how to use resource-based policies with DynamoDB Streams to enable cross-account Lambda consumption. We focus on a common pattern where application workloads live in isolated accounts, and stream processing happens in a centralized or analytics account.

The new aws_dynamodb_global_secondary_index resource treats each GSI as an independent resource with its own lifecycle management. You can use this feature to make capacity adjustments for GSI and tables outside of Terraform. In this post, I demonstrate how to use Terraform’s new aws_dynamodb_global_secondary_index resource to manage GSI drift selectively. I walk you through the limitations of current approaches and guide you through implementing the solution.

The ability to invoke Lambda functions in response to Amazon RDS for SQL Server database events enables powerful use cases such as triggering automated workflows, sending real-time notifications, calling external APIs, and orchestrating complex business processes. In this post, we demonstrate how to enable this integration by using Amazon CloudWatch subscription filters, Amazon SQS, and Amazon SNS to invoke Lambda functions from RDS for SQL Server stored procedures, helping you build responsive, data-driven applications.

In this post, we show how you can use graph algorithms to analyze the results of AWS Entity Resolution and related transactions for the CNP use case. We use several AWS services, including Neptune Analytics, AWS Entity Resolution, Amazon SageMaker notebooks, and Amazon S3.

In this post, we give insights into Aurora DSQL Auto Analyze, a probabilistic and de-facto stateless method to automatically compute DSQL optimizer statistics. Users who are familiar with PostgreSQL will appreciate the similarity to autovacuum analyze.

In this post, we show you how to validate Neo4j Cypher queries before migrating to Neptune using the openCypher Compatibility Checker tool. You can use this tool to identify compatibility issues early in your migration process, reducing migration time and effort.