# AWS Database Blog Official Database Blog of Amazon Web Services This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Resolve Amazon Aurora PostgreSQL lock contention with Database Insights: Part 2 DevFeed: [Resolve Amazon Aurora PostgreSQL lock contention with Database Insights: Part 2]() Original publisher: [Read original article]() Author: Sameer Kumar Published: 2026-09-14T16:02:16Z Content type: tutorial Language: en Sources: [AWS Database Blog]() Topics: [Amazon Aurora](), [Amazon CloudWatch](), [PostgreSQL](), [Concurrency](), [Monitoring](), [Database](), [Amazon EC2](), [Architecture & Design](), [Amazon Web Services](), [AWS CloudFormation]() Tags: [advanced-300](), [amazon-aurora](), [amazon-cloudwatch](), [amazon-ec2](), [aws](), [aws-cloudformation](), [concurrency](), [monitoring](), [performance](), [postgresql](), [rds-for-postgresql](), [simulation](), [technical-how-to]() ### AI overview This tutorial explains how to diagnose and resolve lock contention in Amazon Aurora PostgreSQL using Amazon CloudWatch Database Insights. It demonstrates Lock Analysis and the Lock Tree visualization for identifying blocking sessions, then covers immediate fixes, configuration changes, optimistic concurrency control, asynchronous processing, SKIP LOCKED, and row splitting. ### Source excerpt Part 1 showed how row lock contention degrades Amazon Aurora PostgreSQL throughput. In Part 2, use Amazon CloudWatch Database Insights and its Lock Tree to pinpoint blocking sessions, then resolve contention with query termination, timeout parameters, and architectural patterns such as SKIP LOCKED and row splitting that restore throughput. ## Troubleshooting row lock contention in Amazon Aurora PostgreSQL: Part 1 - Understanding row lock contention in PostgreSQL DevFeed: [Troubleshooting row lock contention in Amazon Aurora PostgreSQL: Part 1 - Understanding row lock contention in PostgreSQL]() Original publisher: [Read original article]() Author: Sameer Kumar Published: 2026-09-14T16:02:08Z Content type: tutorial Language: en Sources: [AWS Database Blog]() Topics: [Amazon Aurora](), [PostgreSQL](), [Concurrency](), [Monitoring](), [Transactions](), [Amazon CloudWatch](), [Amazon RDS](), [Architecture & Design](), [Extension]() Tags: [advanced-300](), [amazon-aurora](), [amazon-cloudwatch](), [amazon-rds](), [architecture](), [availability](), [concurrency](), [database](), [database-performance](), [extension](), [locks](), [monitoring](), [postgresql](), [production](), [rds-for-postgresql](), [technical-how-to](), [transactions](), [troubleshooting]() ### AI overview This first part of a two-part series explains row lock contention in PostgreSQL and Amazon Aurora PostgreSQL. It covers how concurrent transactions competing for the same rows can reduce throughput and cause timeouts despite healthy CPU and I/O, then introduces PostgreSQL locking internals and monitoring techniques using system views, functions, the pgrowlocks extension, and log_lock_waits. The article notes that the same behavior and investigation approach apply to Amazon RDS for PostgreSQL. ### Source excerpt Row lock contention can collapse database throughput during a flash sale even when CPU and I/O look healthy. In Part 1 of this series, learn how PostgreSQL row locking works and how to monitor lock contention in Amazon Aurora PostgreSQL and Amazon RDS for PostgreSQL using system views, the pgrowlocks extension, and the log_lock_waits parameter. ## Run DuckDB analytics on your Amazon DynamoDB data with zero-ETL DevFeed: [Run DuckDB analytics on your Amazon DynamoDB data with zero-ETL]() Original publisher: [Read original article]() Author: Lee Hannigan Published: 2026-09-11T14:53:57Z Content type: tutorial Language: en Sources: [AWS Database Blog]() Topics: [Databases](), [Amazon S3](), [Apache Iceberg](), [AWS Lambda](), [AWS IAM](), [Latency]() Tags: [advanced-300](), [amazon-dynamodb](), [amazon-s3-tables](), [analytics](), [apache-iceberg](), [aws-lambda](), [duckdb](), [how-to](), [iam](), [integration](), [sql](), [technical-how-to]() ### AI overview This tutorial explains how to run ad hoc SQL analytics on Amazon DynamoDB data with DuckDB through a zero-ETL replication flow. ### Source excerpt Run ad hoc SQL analytics on your Amazon DynamoDB data with DuckDB. A zero-ETL integration replicates your table into Apache Iceberg tables on Amazon S3 Tables, and an AWS Lambda function running DuckDB serves SQL queries through an IAM-authorized function URL. ## Building async Python applications with Tortoise ORM and Amazon Aurora DSQL DevFeed: [Building async Python applications with Tortoise ORM and Amazon Aurora DSQL]() Original publisher: [Read original article]() Author: Lasita Bhattacharya Published: 2026-09-09T15:27:43Z Content type: tutorial Language: en Sources: [AWS Database Blog]() Topics: [DSQL](), [Concurrency](), [AWS IAM](), [Database](), [CRUD]() Tags: [advanced-300](), [amazon-aurora](), [concurrency](), [database](), [dsql](), [how-to](), [iam](), [orm](), [postgresql](), [python](), [rideshare](), [serverless](), [technical-how-to]() ### AI overview A tutorial for building a high-concurrency asynchronous Python rideshare application with Tortoise ORM and Amazon Aurora DSQL. It covers UUID-based models, IAM-authenticated asyncpg connections, OCC retry handling, and asynchronous CRUD operations. ### Source excerpt Build a high-concurrency async Python rideshare application with Tortoise ORM and Amazon Aurora DSQL. This post walks through the key adaptations: UUID primary keys, IAM-authenticated asyncpg connections with a connection-pool patch, individual DDL execution, and optimistic concurrency control (OCC) retry logic. ## From noise to signal: Monitoring Amazon DocumentDB like a pro DevFeed: [From noise to signal: Monitoring Amazon DocumentDB like a pro]() Original publisher: [Read original article]() Author: Deepak Deepesh Published: 2026-09-09T15:23:20Z Content type: tutorial Language: en Sources: [AWS Database Blog]() Topics: [Amazon DocumentDB](), [site-reliability-engineering](), [Amazon CloudWatch Logs](), [Database]() Tags: [amazon](), [amazon-cloudwatch](), [amazon-cloudwatch-logs](), [amazon-documentdb](), [best-practices](), [database](), [devops](), [expert-400](), [health](), [how-to](), [logs](), [metrics](), [monitoring](), [on-call](), [operations](), [performance](), [production]() ### AI overview A tutorial on monitoring production Amazon DocumentDB clusters with tiered alerts, CloudWatch metrics, Performance Insights, profiler logs, slow-query diagnosis, and garbage-collection health checks. ### Source excerpt Learn how to build a tiered alerting strategy for Amazon DocumentDB by organizing CloudWatch metrics into Critical, Warning, and Advisory tiers, diagnosing slow queries through three complementary lenses, and monitoring garbage collection health before it becomes a cluster-wide incident. ## Getting started with Oracle Database@AWS: A complete onboarding guide DevFeed: [Getting started with Oracle Database@AWS: A complete onboarding guide]() Original publisher: [Read original article]() Author: Raghu Soma Published: 2026-09-08T16:59:29Z Content type: tutorial Language: en Sources: [AWS Database Blog]() Topics: [Oracle Database](), [cloud-infrastructure](), [AWS IAM]() Tags: [aws](), [aws-marketplace](), [best-practices](), [database](), [guide](), [iam](), [infrastructure](), [intermediate-200](), [onboarding](), [oracle](), [oracle-database-aws]() ### AI overview A step-by-step guide to onboarding Oracle Database@AWS, covering service selection, AWS Marketplace offers, prerequisites, OCI tenancy linking, and IAM configuration before provisioning. ### Source excerpt A practical, step-by-step guide to getting Oracle Database@AWS up and running. It covers the five procurement and onboarding steps, from securing your AWS Marketplace offer through validating prerequisites, linking your OCI tenancy, and configuring IAM, so you can move from purchase to a provisioning-ready environment. ## Monitor self-managed databases with Amazon CloudWatch Database Insights DevFeed: [Monitor self-managed databases with Amazon CloudWatch Database Insights]() Original publisher: [Read original article]() Author: Nirupam Datta Published: 2026-09-03T18:10:43Z Content type: article Language: en Sources: [AWS Database Blog]() Topics: [Databases](), [Amazon EC2](), [dashboards]() Tags: [amazon-aurora](), [amazon-cloudwatch](), [amazon-ec2](), [amazon-rds](), [announcements](), [dashboards](), [database](), [intermediate-200](), [monitoring](), [postgresql](), [sql]() ### AI overview Amazon CloudWatch Database Insights now monitors self-managed PostgreSQL on Amazon EC2 alongside Aurora and Amazon RDS databases from one console. ### Source excerpt Amazon CloudWatch Database Insights now extends to self-managed databases. Monitor self-managed PostgreSQL on Amazon EC2 alongside your Amazon Aurora and Amazon RDS fleet from a single console, with the same DB Load, Top SQL, and wait event analysis you use for managed databases. ## Troubleshoot AWS Advanced JDBC Wrapper configuration for Aurora Global Database write forwarding DevFeed: [Troubleshoot AWS Advanced JDBC Wrapper configuration for Aurora Global Database write forwarding]() Original publisher: [Read original article]() Author: Hema Saminathan Published: 2026-09-03T16:26:20Z Content type: tutorial Language: en Sources: [AWS Database Blog]() Topics: [configuration](), [Database](), [MySQL](), [Latency]() Tags: [advanced-300](), [amazon-aurora](), [aws](), [drivers](), [global](), [latency](), [mysql](), [postgresql](), [technical-how-to]() ### AI overview A configuration troubleshooting guide for the AWS Advanced JDBC Wrapper with Aurora Global Database write forwarding. It explains Region-specific settings for primary and secondary Regions, including MySQL-specific write-forwarding behavior. ### Source excerpt Configuring the AWS Advanced JDBC Wrapper for Amazon Aurora Global Database with write forwarding requires Region-specific settings, and misconfiguration causes latency spikes and connection failures. This post walks through the correct dialect, plugins, host patterns, and write forwarding settings for the primary and secondary Regions. ## Introducing strands-dynamodb-storage: Durable agent storage for the Strands Agents SDK DevFeed: [Introducing strands-dynamodb-storage: Durable agent storage for the Strands Agents SDK]() Original publisher: [Read original article]() Author: Lee Hannigan Published: 2026-09-02T15:48:06Z Content type: release Language: en Sources: [AWS Database Blog]() Topics: [AI Development](), [Database](), [SDKs](), [Amazon Bedrock](), [AWS Lambda]() Tags: [advanced-300](), [ai-agents](), [amazon-dynamodb](), [announcements](), [memory](), [open-source](), [python](), [storage](), [strands-agents](), [typescript](), [vector]() ### AI overview Amazon Web Services announces strands-dynamodb-storage, an open-source DynamoDB backend for the Strands Agents SDK. It stores agent session state, long-term memories, and transcripts in one DynamoDB table and supports semantic memory recall through a vector index. ### Source excerpt Announcing strands-dynamodb-storage, an open source Amazon DynamoDB storage backend for the Strands Agents SDK. Back a Strands agent's session state, long-term memories, and transcripts with one DynamoDB table in your own account, and give the agent semantic recall with a vector index on that same table. ## SQL Server to Aurora PostgreSQL conversion with AI agents for AWS DMS DevFeed: [SQL Server to Aurora PostgreSQL conversion with AI agents for AWS DMS]() Original publisher: [Read original article]() Author: Mayur Aggarwal Published: 2026-09-01T15:19:13Z Content type: tutorial Language: en Sources: [AWS Database Blog]() Topics: [AWS Database Migration Service](), [sql-server](), [Database Migration](), [Model Context Protocol](), [Databases]() Tags: [advanced-300](), [ai-agents](), [amazon-aurora](), [aws-database-migration-service](), [database-migration](), [how-to](), [mcp](), [postgresql](), [technical-how-to]() ### AI overview Tutorial on using AI agents with AWS DMS Schema Conversion to convert SQL Server schemas to Aurora PostgreSQL through natural-language workflows. ### Source excerpt Learn how to use AI agents with AWS DMS Schema Conversion to orchestrate SQL Server to Amazon Aurora PostgreSQL schema conversion through natural language. See how the conversion engine processes T-SQL, how the agent drives the workflow, and how to interpret and resolve CRITICAL action items using decision frameworks for common incompatibilities. ## Fix circular role dependencies before upgrading Amazon RDS and Amazon Aurora PostgreSQL DevFeed: [Fix circular role dependencies before upgrading Amazon RDS and Amazon Aurora PostgreSQL]() Original publisher: [Read original article]() Author: Ravi Teja Adabala Published: 2026-08-31T23:42:55Z Content type: article Language: en Sources: [AWS Database Blog]() Topics: [PostgreSQL](), [RDS for PostgreSQL](), [Amazon Aurora](), [Amazon RDS](), [SQL](), [postgresql clusters](), [Graphs]() Tags: [amazon-aurora](), [amazon-rds](), [database](), [how-to](), [intermediate-200](), [postgresql](), [rds-for-postgresql](), [sql](), [technical-how-to]() ### AI overview This article explains how circular PostgreSQL role dependencies can cause major version upgrades of Amazon RDS for PostgreSQL and Amazon Aurora PostgreSQL to stall or roll back when upgrading from PostgreSQL 14 or earlier to PostgreSQL 15 or later. It describes a diagnostic query and steps to resolve the dependencies before the upgrade. ### Source excerpt Circular role dependencies can stall or roll back a major version upgrade of Amazon RDS for PostgreSQL or Amazon Aurora PostgreSQL when you move from PostgreSQL 14 or earlier to 15 or later. Learn why this happens, how to detect it with a single pre-upgrade query, and how to clear it before you upgrade. ## Provision a secure Amazon DocumentDB cluster with Terraform DevFeed: [Provision a secure Amazon DocumentDB cluster with Terraform]() Original publisher: [Read original article]() Author: Sourav Kundu Published: 2026-08-31T20:24:23Z Content type: tutorial Language: en Sources: [AWS Database Blog]() Topics: [Amazon DocumentDB](), [iac-security](), [Cryptography](), [passwords]() Tags: [advanced-300](), [amazon-cloudwatch](), [amazon-documentdb](), [amazon-vpc](), [authentication](), [encryption](), [infrastructure-as-code](), [monitoring](), [secrets](), [technical-how-to](), [terraform]() ### AI overview A walkthrough for provisioning a secure Amazon DocumentDB 8.0 cluster with Terraform. It applies infrastructure-as-code security controls including private VPC subnets, TLS, KMS encryption, secrets-managed passwords, security groups, and encrypted monitoring logs. ### Source excerpt Learn how to provision a secure Amazon DocumentDB cluster using Terraform. This post applies infrastructure-as-code best practices with encryption, AWS Secrets Manager authentication, network isolation, and encrypted monitoring. ## CORTO's billion-scale legal semantic search with Aurora PostgreSQL pgvector DevFeed: [CORTO's billion-scale legal semantic search with Aurora PostgreSQL pgvector]() Original publisher: [Read original article]() Author: Anisa Dean Published: 2026-08-26T16:40:00Z Content type: article Language: en Sources: [AWS Database Blog]() Topics: [AI search](), [tenant data protection]() Tags: [advanced-300](), [ai](), [amazon-aurora](), [architecture](), [compliance](), [customer-solutions](), [embedding](), [postgresql](), [postgresql-compatible](), [production](), [scale](), [search]() ### AI overview CORTO describes scaling legal semantic search on Amazon Aurora PostgreSQL with pgvector for billions of documents and vectors. The article focuses on embedding choices, multi-tenant isolation, cost efficiency, and sub-second query performance. ### Source excerpt How CORTO scaled Amazon Aurora PostgreSQL with pgvector to 7.6 billion vectors and 2.5 billion documents in production, delivering sub-second legal search for 10,000+ law firms at 75% lower storage cost. ## How Channel Corporation modernized their architecture with Amazon DynamoDB, Part 3: User and Badge DevFeed: [How Channel Corporation modernized their architecture with Amazon DynamoDB, Part 3: User and Badge]() Original publisher: [Read original article]() Author: Haibin (Binu) Lee Published: 2026-08-25T18:39:51Z Content type: article Language: en Sources: [AWS Database Blog]() Topics: [data](), [Amazon Web Services](), [migration](), [Amazon S3](), [Latency](), [App]() Tags: [advanced-300](), [amazon](), [amazon-dynamodb](), [app](), [architecture](), [aws](), [customer-solutions](), [data](), [latency](), [migration]() ### AI overview Channel Corporation describes how it addressed DynamoDB User table throttling by separating role-specific data, including Badge data, from an all-purpose User table. The article explains the effects of bursty badge writes, item-size-based write capacity consumption, and transaction processing, and introduces an online migration approach using DynamoDB Export and Import with Amazon S3 and AWS Glue. ### Source excerpt Channel Corporation shares how they split their all-purpose Amazon DynamoDB User table into role-specific tables, moving Badge data into a dedicated UserBadge table to stop transaction-conflict throttling and GSI back pressure, and how they ran a zero-downtime online migration using DynamoDB Export and Import with Amazon S3 and AWS Glue. ## Cross-database access using module signing on Amazon RDS for SQL Server DevFeed: [Cross-database access using module signing on Amazon RDS for SQL Server]() Original publisher: [Read original article]() Author: Nirupam Datta Published: 2026-08-24T18:07:08Z Content type: tutorial Language: en Sources: [AWS Database Blog]() Topics: [Amazon RDS](), [RDS for SQL Server](), [sql-server](), [Security](), [Authorization](), [migration]() Tags: [advanced-300](), [amazon](), [amazon-rds](), [how-to](), [microsoft](), [migration](), [post](), [rds-for-sql-server](), [security](), [sql](), [technical-how-to]() ### AI overview This post explains how to enable secure cross-database access on fully managed Amazon RDS for SQL Server using certificate-based module signing. It presents module signing as an RDS-compatible alternative to TRUSTWORTHY, with permissions limited to specific stored procedures and aligned with least privilege. ### Source excerpt If you need cross-database access on Amazon RDS for SQL Server but cannot enable TRUSTWORTHY, module signing with certificates is the secure, RDS-compatible alternative. This post shows how to grant cross-database permissions to specific stored procedures without TRUSTWORTHY and with a stronger, least-privilege security posture. ## Migrate multilingual full-text search from SQL Server to PostgreSQL DevFeed: [Migrate multilingual full-text search from SQL Server to PostgreSQL]() Original publisher: [Read original article]() Author: Ken Zhang Published: 2026-08-20T20:34:44Z Content type: tutorial Language: en Sources: [AWS Database Blog]() Topics: [PostgreSQL](), [sql-server](), [SQL](), [Amazon Aurora](), [Amazon RDS](), [tokenization]() Tags: [advanced-300](), [amazon](), [amazon-aurora](), [amazon-rds](), [aws](), [blog](), [how-to](), [postgresql](), [postgresql-compatible](), [rds-for-postgresql](), [sql](), [technical-how-to](), [tokenization]() ### AI overview This tutorial explains how to migrate multilingual full-text search from SQL Server to PostgreSQL while preserving comparable behavior. It covers collations, text search configurations, language-specific tokenization, accent-insensitive search, synonym expansion, and validation testing on Amazon Aurora PostgreSQL and Amazon RDS for PostgreSQL. ### Source excerpt 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 DevFeed: [Understand memory management in Amazon RDS for PostgreSQL to avoid out of memory]() Original publisher: [Read original article]() Author: Ankita Singh Published: 2026-08-19T22:21:10Z Content type: tutorial Language: en Sources: [AWS Database Blog]() Topics: [PostgreSQL](), [Memory Leaks](), [Processes](), [Linux](), [Monitoring](), [Amazon Web Services](), [Availability]() Tags: [advanced-300](), [amazon-aurora](), [amazon-cloudwatch](), [amazon-rds](), [availability](), [aws](), [memory](), [monitoring](), [postgresql](), [postgresql-compatible](), [processes](), [production](), [rds-for-postgresql](), [technical-how-to]() ### AI overview A practical guide to PostgreSQL memory management on Amazon RDS for PostgreSQL and Amazon Aurora PostgreSQL. It explains how memory is allocated, how memory-intensive queries and diagnostic signals can be identified, and how to diagnose, prevent, and recover from out-of-memory events and excessive disk spilling. ### Source excerpt 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 DevFeed: [Characterizing SQL\*Net latency in your application for Oracle Database@AWS migrations]() Original publisher: [Read original article]() Author: Sameer Malik Published: 2026-08-18T18:17:28Z Content type: article Language: en Sources: [AWS Database Blog]() Topics: [Oracle Database](), [Latency](), [migration](), [Amazon Web Services](), [Database](), [Network]() Tags: [advanced-300](), [amazon-ec2](), [amazon-elastic-container-service](), [amazon-elastic-kubernetes-service](), [amazon-vpc](), [aws](), [best-practices](), [database](), [latency](), [migration](), [network](), [oracle](), [oracle-database-aws](), [performance]() ### AI overview This article presents a methodology for assessing SQL*Net latency sensitivity before migrating Oracle workloads to Oracle Database@AWS. It focuses on identifying workloads with fast, frequent, and concentrated SQL activity, then quantifying the impact of added network round-trip latency in a multi-cloud architecture. It also discusses reducing client-server round trips through stored procedures, array fetches, and bulk processing. ### Source excerpt 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 DevFeed: [Integrate your Spring Boot application with Amazon ElastiCache using Spring Data Valkey]() Original publisher: [Read original article]() Author: Chris Gillespie Published: 2026-08-18T16:38:30Z Content type: tutorial Language: en Sources: [AWS Database Blog]() Topics: [App](), [AWS IAM](), [telemetry]() Tags: [amazon-elasticache](), [applications](), [aws-iam](), [cache](), [cli](), [encryption](), [how-to](), [integration](), [intermediate-200](), [observability](), [opentelemetry](), [performance](), [redis](), [serverless](), [spring-boot](), [technical-how-to]() ### AI overview A tutorial for adding Amazon ElastiCache caching to a Spring Boot application with Spring Data Valkey. It covers dependencies, Valkey configuration, TTL settings, encryption, and migration compatibility with Spring Data Redis. ### Source excerpt 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 DevFeed: [Scale pgvector with binary quantization on Amazon Aurora PostgreSQL]() Original publisher: [Read original article]() Author: Steve Dille Published: 2026-08-18T16:37:22Z Content type: tutorial Language: en Sources: [AWS Database Blog]() Topics: [Amazon Aurora](), [PostgreSQL](), [quantization](), [AI search](), [Compression](), [Embeddings](), [Cache](), [Latency](), [datasets](), [configuration](), [postgresql clusters](), [Database]() Tags: [advanced-300](), [ai](), [amazon-aurora](), [cache](), [compression](), [datasets](), [embeddings](), [latency](), [postgresql](), [postgresql-compatible](), [quantization](), [rds-for-postgresql](), [search](), [technical-how-to](), [validation](), [vector]() ### AI overview This practical guide explains how to use binary quantization with reranking in pgvector to scale HNSW vector search on Amazon Aurora PostgreSQL. It covers index-size reduction, performance and recall tradeoffs, sizing, validation, and suitable operating conditions for datasets ranging from 5 million to 100 million vectors. ### Source excerpt 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 DevFeed: [Migrate Oracle Materialized Views with AWS DMS and Fast Refresh]() Original publisher: [Read original article]() Author: Abhilash Negi Published: 2026-08-18T16:36:11Z Content type: tutorial Language: en Sources: [AWS Database Blog]() Topics: [AWS Database Migration Service](), [migration]() Tags: [advanced-300](), [analytics](), [architecture](), [aws](), [aws-database-migration-service](), [cloud](), [data](), [database](), [database-migration](), [datasets](), [how-to](), [latency](), [logs](), [migration](), [oracle](), [performance](), [real-time](), [replication](), [technical-how-to](), [testing](), [validation](), [workflow]() ### AI overview A tutorial for configuring Oracle materialized views with Fast Refresh and materialized view logs so AWS DMS can replicate incremental CDC changes instead of complete refreshes. ### Source excerpt 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 DevFeed: [Amazon Aurora DSQL observability concepts and usage with Amazon CloudWatch]() Original publisher: [Read original article]() Author: James Morle Published: 2026-08-17T23:37:48Z Content type: article Language: en Sources: [AWS Database Blog]() Topics: [DSQL](), [telemetry]() Tags: [amazon](), [amazon-aurora](), [amazon-cloudwatch](), [cost](), [dash](), [database](), [diagnostics](), [dsql](), [insights](), [intermediate-200](), [metrics](), [monitoring](), [mysql](), [observability](), [performance](), [postgresql](), [sql](), [technical-how-to]() ### AI overview This article explains Amazon Aurora DSQL's time-based observability model and its link to performance and DPU-based cost. It introduces DASH session sampling and using Amazon CloudWatch Database Insights, PromQL, and system diagnostics to identify bottlenecks. ### Source excerpt 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.