Category: Technical How-to

Learn how to build, train, and score a credit risk machine learning model entirely in SQL using Oracle Machine Learning for SQL (OML4SQL) on Amazon RDS for Oracle. The built-in AutoML feature selects the best algorithm automatically, with no data movement and no external machine learning platforms.

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.

Amazon Keyspaces supports two approaches for modeling complex data: user-defined types (UDTs) and Protocol Buffers. This post shows how to create and manage UDTs, implement Protobuf serialization, and choose between the two based on your application needs.

Learn how to use the calling services feature in Amazon CloudWatch Database Insights to identify which applications are calling your databases and view their performance metrics, so you can pinpoint root causes and contact the right team in minutes rather than hours.

Learn how to migrate from self-managed Apache Cassandra to Amazon Keyspaces with zero downtime by deploying an extended version of the ZDM Proxy on AWS Fargate. This post covers the single CloudFormation stack, the six migration phases from historical data loading through validation and cutover, and security and cost best practices for production workloads.

Learn a complete database administrator workflow for monitoring, diagnosing, and resolving T-SQL query performance issues on Amazon RDS for SQL Server. This post shows how to use Amazon CloudWatch Database Insights, Query Store, and Resource Governor together to detect regressions, diagnose execution plan changes, and isolate analytical workloads.

In this post, we provide practical guidance for designing applications that scale effectively with the Amazon Aurora DSQL distributed architecture. You will learn how to identify common patterns that limit scalability, apply proven design patterns that distribute workload efficiently, and implement transaction strategies optimized for Aurora DSQL. We cover primary key selection, schema design principles, indexing strategies, and multi-Region optimization, while maintaining full ACID (atomicity, consistency, isolation, and durability) compliance across AWS Regions.

In this post, we show you how to deploy a serverless pipeline that creates an irreversibly redacted, queryable archive of Amazon RDS for PostgreSQL audit logs. The pipeline permanently removes Social Security numbers (SSNs), credit cards, email, names, and more than 30 types of personally identifiable information (PII) before storing clean logs in Amazon S3 for query through Amazon Athena.

Migrate a self-managed PostgreSQL database on Amazon EC2 to Amazon RDS for PostgreSQL or Amazon Aurora PostgreSQL-Compatible Edition using the auto-migration capability in the Amazon RDS console, powered by AWS Database Migration Service (AWS DMS) homogeneous data migrations.

In this post, we show how you can build a multi-Region active-active API using Prisma ORM and Amazon Aurora DSQL.