Kunal Ghosh

If you manage data in Amazon Aurora MySQL-Compatible Edition and want to make it available for analytics, machine learning (ML), or cross-service querying in a modern lakehouse format, you’re not alone. Organizations often need to run analytics, build ML models, or join data across multiple sources. These are examples of workloads that can be resource-intensive and impractical to run directly against a transactional database.

Artificial Intelligence Organizations need seamless access to their structured data repositories to power intelligent AI agents. However, when these resources span multiple AWS accounts integration challenges can arise. This post explores a practical solution for connecting Amazon Bedrock agents to knowledge bases in Amazon Redshift clusters residing in different AWS accounts.