Category: Advanced (300)

In this post, we explore how pgactive can perform rolling major version upgrades for Amazon Relational Database Service (Amazon RDS) for PostgreSQL, allowing for a smoother transition with reduced impact on your applications.

AWS Database Migration Service is a cloud service designed to simplify the process of migrating and replicating databases, data warehouses and other data stores. It offers a comprehensive solution for both homogeneous and heterogeneous database migrations, facilitating transitions between different database platforms. The migration process typically involves two major phases: Migration of existing data (full […]

Many organizations run IBM Db2 databases across multiple physical servers or virtual machines. This approach leads to resource investments in infrastructure, management, and licensing. Additionally, advancements in hardware technology, increased CPU capacities, and database engine enhancements result in underutilized servers if not rightsized at the outset. To optimize resource utilization, organizations can explore the following […]

In this post, we dive into the various architecture patterns and options available for both compute and storage layers while configuring your self-managed Oracle databases on Amazon EC2 to comply with your HA and DR requirements.

In this post, we show you several long-term data backup strategies and how to effectively implement them in the AWS environment, with a focus on Amazon Relational Database Service (Amazon RDS) and Amazon Aurora.

When using Secrets Manager to manage your master user passwords, you cannot create new read replicas for your database instance. This applies to all DB engines except Amazon RDS for SQL Server, potentially impacting your organization’s ability to efficiently scale its read operations while maintaining secure credential practices. In this post, we present a solution that automates the process of rotating passwords for a primary instance with read replicas while maintaining secure credential management practices. This approach allows you to take advantage of the benefits of both read scaling and automated credential rotation.

In this post, we highlight the scaling and performance challenges Mindbody was facing due to an increase in their data growth. We also present the root cause analysis and recommendations for adopting to Aurora Optimized Reads, outlining the steps taken to address these issues. Finally, we discuss the benefits Mindbody realized from implementing these changes, including enhanced query performance, significant cost savings, and improved price predictability.

In this post, we discuss the fully managed approach using Amazon Bedrock Knowledge Bases to simplify the integration of the data source with your generative AI application using Aurora. Amazon Bedrock is a fully managed service that makes foundation models (FMs) from leading AI startups and Amazon available through an API, so you can choose from a wide range of FMs to find the model that is best suited for your use case.

In this post, we explore the process of building a multi-tenant generative AI application using Aurora PostgreSQL-Compatible for vector storage. In Part 1 (this post), we present a self-managed approach to building the vector search with Aurora. In Part 2, we present a fully managed approach using Amazon Bedrock Knowledge Bases to simplify the integration of the data sources, the Aurora vector store, and your generative AI application.

Oracle Multitenant feature is available in Oracle database from 12cR1 (12.1.0.1) and later. This enables customers to use multiple PDBs in a single Oracle database, facilitating better manageability and consolidation of environments. In Oracle Multitenant architecture, there are various user management approaches available that can be used to create and manage user accounts in the container database (CDB) and PDBs. In this post we discuss the options for managing users and how they can be set up and used for different scenarios.