Category: Advanced (300)

In this post, we highlight often overlooked architectural designs and the inherent features of Aurora that optimize costs when deploying an open source database. The following sections examine various use cases, contrasting typical self-managed database configurations and their associated costs with the equivalent solution on Aurora, highlighting potential cost savings and operational efficiencies.

In this post, we show you a worked example of taking an existing application that works on PostgreSQL databases and adapting it to work with an Aurora DSQL database. In addition to adapting for the previously mentioned aspects, we also address some data type incompatibilities and work around some limits that currently exist in Aurora DSQL.

When assisting customers with migrating their workloads from SQL Server to PostgreSQL, we often encounter a scenario where the PIVOT function is used extensively for generating dynamic reports. In this post, we show you how to use the crosstab function, provided by PostgreSQL’s tablefunc extension, to implement functionality similar to SQL Server’s PIVOT function, offering greater flexibility.

In this post, we present an approach to using natural language processing (NLP) to query an Amazon Aurora PostgreSQL-Compatible Edition database. The solution presented in this post assumes that an organization has an Aurora PostgreSQL database. We create a web application framework using Flask for the user to interact with the database. JavaScript and Python code act as the interface between the web framework, Amazon Bedrock, and the database.

In this post we introduce a solution that addresses a common challenge faced by many customers: managing costs in multi-tenant applications, particularly for shared databases in Amazon Relational Database Service (Amazon RDS) and Amazon Aurora. This solution uses Amazon RDS Performance Insights and AWS Cost and Usage Reports (CUR) to addresses this challenge. This allows for efficient grouping of tenants within the same RDS or Aurora instances, while helping you implement accurate chargeback models, optimize resource-intensive workloads, and make data-driven decisions for capacity planning.

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.