AWS Database Blog

Amazon Keyspaces now supports the pre-warming feature to provide you with proactive throughput management. With pre-warming, you can set minimum warm throughput values that your table can handle instantly, avoiding the cold start delays that occur during dynamic partition splits. In this post, we discuss the Amazon Keyspaces pre-warming feature capabilities and demonstrate how it can enhance your throughput performance.

In this post, you will learn how to address IP address exhaustion challenges when working with Amazon RDS Proxy. For customers experiencing IP exhaustion with RDS Proxy, migrating to IPv6 address space can be an effective solution if your workload supports IPv6. This post focuses on workloads that cannot support IPv6 address space and provides an alternative approach using IPv4 subnet expansion. The solution focuses on expanding your Amazon Virtual Private Cloud (Amazon VPC) CIDR range, establishing new subnets, and executing a carefully planned switching of your proxy to a new subnet configuration.

In this post, you learn how to select the appropriate code page and collation sequence when migrating from Db2 mainframe (z/OS) to Amazon RDS for Db2 on Linux. You explore the differences between mainframe CCSIDs and Db2 LUW code pages, understand character compatibility requirements, and discover how to prevent data truncation and maintain consistent sorting behavior across platforms.

In this post, we show you how to build a centralized dashboard for monitoring Amazon RDS configurations across your organization by using AWS Config and Amazon Quick Suite. This solution delivers detailed insights across different areas, such as summary metrics, backup configurations, security posture, engine and support information, extended configurations, and resource tagging.

Amazon Redshift transforms how organizations analyze JSON data by combining the analytical power of a columnar data warehouse with robust JSON processing capabilities. By using Amazon Redshift SUPER datatype, you can efficiently store, query, and analyze complex hierarchical data alongside traditional structured data without sacrificing performance. This post focuses on JSON features of Amazon Redshift.

In this post, we help you plan your upgrade from PostgreSQL version 13 before standard support ends on February 28, 2026. We discuss the key benefits of upgrading, breaking changes to consider, and multiple upgrade strategies to choose from.

In 2023, Tradeshift migrated one of its core PostgreSQL databases from self-managed Amazon Elastic Compute Cloud (Amazon EC2) instances to Amazon Relational Database Service (Amazon RDS) for PostgreSQL. The decision followed mounting operational risks and performance limits that made the existing setup increasingly unsustainable. Tradeshift needed a managed solution that could reduce downtime risk, improve observability, and simplify ongoing operations. Amazon RDS met those requirements. In this post, we explain why we migrated to Amazon RDS, how we executed the migration, and highlight the invaluable benefits it delivered in terms of safety, flexibility, and audit compliance.

MaiCoin is a leading cryptocurrency exchange and brokerage platform in Taiwan. The MaiCoin platform previously ran on a set of Amazon ElastiCache deployment clusters on Redis OSS. This post explores MaiCoin’s practical approaches using RedisShake for migrating from Amazon ElastiCache for Redis OSS to Amazon ElastiCache for Valkey using blue/green deployment strategies.

Amazon RDS for MySQL and Amazon Aurora MySQL provide built-in audit logging capabilities, but customers might need to export and store these logs for long-term retention and analysis. Amazon S3 offers an ideal destination, providing durability, cost-effectiveness, and integration with various analytics tools. In this post, we explore two approaches for exporting MySQL audit logs to Amazon S3: either using batching with a native export to Amazon S3 or processing logs in real time with Amazon Data Firehose.

In Amazon Relational Database Service (Amazon RDS) for SQL Server, database visibility is configured using a dedicated stored procedure. In this post, we demonstrate tenant isolation at the visibility level, preventing tenants from seeing database names belonging to other customers while maintaining their access to their own resources. This solution addresses an important architectural consideration in multi-tenant SQL Server environments where database names might reveal tenant information. By using the Amazon RDS for SQL Server custom stored procedure msdb.dbo.rds_manage_view_db_permission, users can effectively control database visibility on a per-login basis while maintaining full application functionality.