Category: Expert (400)

Oracle Database@AWS delivers enterprise-grade database capabilities through database services that use Oracle Exadata infrastructure managed by Oracle Cloud Infrastructure (OCI) within Amazon Web Services (AWS) data centers. High availability (HA) and disaster recovery (DR) options are important aspects to consider when migrating or deploying your critical database in Oracle Database@AWS to help make sure the architecture can meet the service level agreement (SLA) of the application. This post will help you implement and maintain Data Guard configurations that follow Oracle’s Maximum Availability Architecture (MAA) best practices and the AWS Well-Architected Framework. We will show how to select the right network architecture and configure Data Guard associations for both cross-AZ and cross-Region deployments, making sure your applications maintain seamless connectivity during role transitions.

In this post, we guide you on how to run resilience tests on Amazon ElastiCache using AWS Fault Injection Service and how you can use it to strengthen the resilience strategy of your application.

In this post, we give insights into Aurora DSQL Auto Analyze, a probabilistic and de-facto stateless method to automatically compute DSQL optimizer statistics. Users who are familiar with PostgreSQL will appreciate the similarity to autovacuum analyze.

Amazon Aurora DSQL employs an active-active distributed database design, wherein all database resources are peers and serve both write and read traffic within a Region and across Regions. This design facilitates synchronous data replication and automated zero data loss failover for single and multi-Region Aurora DSQL clusters. In this post, I discuss the individual components and the responsibilities of a multi-Region distributed database to provide an ACID-compliant, strongly consistent relational database.

In this third post of the series, I examine the end-to-end processing of the two transaction types in Aurora DSQL: read-only and read-write. Amazon Aurora DSQL doesn’t have write-only transactions, since it’s imperative to verify the table schema or ensure the uniqueness of primary keys on each change – which results them being read-write transactions as well.

In this post, we walk through the steps of integrating Oracle Database@AWS (ODB@AWS) with Amazon Bedrock for by creating a RAG assistant application using an Amazon Titan embedding model in Amazon Bedrock and vectors stored in Oracle AI Database 26ai.

In this post, we show an algorithm to efficiently compare two Amazon DynamoDB tables and find the differences between their items. We provide an example where two tables, each containing approximately half a billion items, are compared in less than 7 minutes, for less than $10.

Part 1 of this series showed how to rate-limit calls to Amazon DynamoDB by using Python Boto3 event hooks. In this post, I expand on the concept and show how to rate-limit calls in a distributed environment, where you want a maximum allowed rate across the full set of clients but can’t use direct client-to-client communication.

In this post, I present a technique where a Python script making calls to Amazon DynamoDB can rate limit its consumption of read and write capacity units. The technique uses Boto3 event hooks to apply the rate limiting without having to modify the client code performing the read and write calls.

This post demonstrates how to implement SSL encryption for in-transit data protection when connecting Oracle Real Application Clusters (Oracle RAC) as a source to AWS Database Migration Service (AWS DMS). Additionally, it covers the unique steps required to configure SSL for Oracle Automatic Storage Management (Oracle ASM) instances.