Category: Expert (400)

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.

Oracle Database@AWS (ODB@AWS) is an offering you can use to access Oracle Exadata infrastructure managed by Oracle Cloud Infrastructure (OCI) within Amazon Web Services (AWS) data centers. You can use ODB@AWS to migrate your Oracle Exadata workloads to AWS while maintaining the same performance and features as your on-premises Oracle Exadata deployments. You benefit from […]

AWS DMS accommodates a broad range of source and target data repositories, such as relational databases, data warehouses, and NoSQL databases. Proper preparation and design are vital for a successful migration process, especially when it comes to optimizing performance and addressing potential delay issues. In this blog post, we offer guidance about recognizing potential root causes of complete load and CDC delays early in the process and provide suggestions for optimally clustering tables to achieve the best performance for an AWS DMS task.