AWS Database Blog
Migrate multilingual full-text search from SQL Server to PostgreSQL
Migrating full-text search from SQL Server to PostgreSQL can silently change results because the engines handle text, linguistics, and accents differently. This post shows how to reproduce SQL Server full-text search on Amazon Aurora PostgreSQL and Amazon RDS for PostgreSQL, covering collation, tokenization, accent-insensitive search, and synonyms.
Understand memory management in Amazon RDS for PostgreSQL to avoid out of memory
PostgreSQL out-of-memory (OOM) events and excessive disk spilling are among the most common production incidents on Amazon RDS for PostgreSQL and Amazon Aurora PostgreSQL. Learn how PostgreSQL allocates and consumes memory, how to identify memory-intensive queries, and how to diagnose, prevent, and recover from OOM events on both engines.
Characterizing SQL*Net latency in your application for Oracle Database@AWS migrations
Oracle Database@AWS places Oracle Exadata infrastructure inside AWS data centers, so SQL*Net latency between your application and the database can change after migration. This post presents the CRET methodology, a three-phase approach using AWR, Active Session History, and SQL Trace, to identify latency-sensitive SQL and quantify the impact before you migrate.
Integrate your Spring Boot application with Amazon ElastiCache using Spring Data Valkey
Learn how to integrate a Spring Boot application with Amazon ElastiCache using Spring Data Valkey for caching. This walkthrough covers adding caching to a serverless cache, plus the advantages of Spring Data Valkey over Spring Data Redis: native AWS IAM authentication, Availability Zone affinity, and OpenTelemetry observability.
Scale pgvector with binary quantization on Amazon Aurora PostgreSQL
Learn how to use binary quantization with reranking (HNSW+BQ) in pgvector to scale vector search to hundreds of millions or billions of vectors on Amazon Aurora PostgreSQL, with practical guidance on index sizing, recall validation, and the scenarios where the approach works best.
Migrate Oracle Materialized Views with AWS DMS and Fast Refresh
Learn how to configure Oracle Materialized Views with Fast Refresh and materialized view logs to enable efficient, incremental AWS DMS change data capture (CDC) replication. This approach eliminates full-reload overhead and achieves near real-time migration of large, multi-table joined views to AWS.
Amazon Aurora DSQL observability concepts and usage with Amazon CloudWatch
Amazon Aurora DSQL offers time-based observability through Amazon CloudWatch Database Insights. Learn how the DSQL observability model, DASH, Database Insights, PromQL, and the system diagnostics AI skill help you find performance bottlenecks and connect session time directly to cost.
Unlocking real-time analytics: Streaming Aurora DSQL changes into Apache Iceberg
Stream Amazon Aurora DSQL change data capture (CDC) events into Apache Iceberg tables on Amazon S3 with Amazon Data Firehose, then query them using Amazon Athena. This post walks through a two-table design that keeps a full audit trail and a current-state view, plus deployment and a dashboard for exploring the results.
Create Oracle Wallet for AWS DMS SSL connections using SQLcl
Learn how to use Oracle SQLcl, a lightweight alternative to the full Oracle Client, to create and manage an Oracle Wallet for SSL connections between AWS DMS and Amazon RDS for Oracle. This post walks through installing SQLcl, adding certificates, testing the SSL connection, and configuring the AWS DMS endpoint.
Faster scaling for Aurora serverless to support agentic AI and other spiky workloads
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.









