Category: Advanced (300)

Aurora serverless now automatically adds 12 Aurora Capacity Units to its current capacity within a second, and continues scaling to 256 ACUs as your workload grows. In this post, we show how an Aurora serverless cluster responds to a sudden workload spike, and compare its throughput against a provisioned db.r8g.xlarge instance using benchmark data.

Microsoft SQL Server 2016 reaches its end of extended support on July 14, 2026. If you run it on Amazon RDS for SQL Server with user-defined CLR assemblies, you must replace them before you upgrade, because CLR is not supported on SQL Server 2017 and later. This post shows you how to find your CLR dependencies and compares four replacement strategies.

Standard upgrade paths break active Debezium CDC replication slots on Amazon Aurora PostgreSQL, forcing hours-long re-snapshots. This post shows how to use native PostgreSQL logical replication to bridge a source and target cluster and cut your Debezium connectors over to the new major version with a brief, measured write pause and no re-snapshot.

Oracle Database 26ai is now available on Amazon RDS for Oracle, bringing generative AI, vector search, and machine learning into the engine. In this post, part 1 of a three-part series, learn how to set up Select AI to run natural language queries against your relational data using foundation models on Amazon Bedrock, all from within a private Amazon RDS instance.

When an Amazon RDS or Amazon Aurora PostgreSQL certificate expires and client trust stores aren’t updated, connections fail without warning. This post shows how to enforce TLS for all PostgreSQL connections, configure client-side certificate verification, and deploy automated monitoring that alerts you before certificate rotation events.

Organizations running Amazon RDS and Amazon Aurora often pay full CloudWatch Logs ingestion rates for database logs they rarely access. This post shows how to build an automated, tag-driven solution that migrates RDS and Aurora CloudWatch log groups from the Standard to the Infrequent Access log class and cuts log ingestion costs by about 50%.

In this post, you learn how to use Amazon OpenSearch Service as a discovery layer that combines full-text, vector, and geospatial search across JSON documents in a single query, using a restaurant discovery app as a working example.

Learn how to configure connection pooling for the AWS Advanced JDBC Wrapper on Amazon Aurora and Amazon RDS. This post explains how the wrapper’s external and internal pooling differ, how to choose between them, and how the JDBC-WRAPPER-CONFIGURATION-ASSISTANT helps you build the right configuration.

Learn how to build zero-downtime write architectures for Amazon Neptune using a write queue pattern with Amazon SQS, Amazon Kinesis Data Streams, or Amazon MSK. By decoupling write acceptance from write execution, your application keeps accepting graph writes during maintenance windows, failovers, and scaling operations.

Many applications that use Amazon DynamoDB for operational data also need vector similarity search, which until now meant running a separate vector database. DynamoDB now supports native vector search. In this post, we build a semantic search application over research paper abstracts using Amazon Bedrock embeddings, and cover how vector search is metered.