Category: Technical How-to

In this post, we show you how to use CodePipeline to streamline your Aurora database deployments. We dive into a detailed architecture and steps for using CodePipeline in conjunction with AWS CodeBuild and AWS Secrets Manager. By the end of this post, you’ll have a clear understanding of how to set up a robust, automated pipeline for your database changes, allowing you to focus on what really matters—delivering value to your customers through innovative features and optimized performance.

We are excited to announce Amazon Timestream for LiveAnalytics as a newly supported target endpoint for AWS Database Migration Service (AWS DMS). This addition allows you to move time-series data from an AWS DMS supported source database to Timestream. In this post, we show you how to use Timestream as a target for an example PostgreSQL source endpoint in AWS DMS.

In this post, we demonstrate how to set up an event-driven workflow to run stored procedures for Amazon RDS for PostgreSQL with AWS Lambda to bridge this gap by securely connecting to an Aurora PostgreSQL database using AWS Secrets Manager, making sure that stored procedures can be managed in the cloud. We explore the step-by-step process, discuss the advantages of this approach, and address the limitations of invoking stored procedures from Lambda functions.

Amazon Web Services (AWS) users frequently use multiple accounts, organizing them efficiently with AWS Organizations. This system structures the accounts hierarchically and groups them into Organizational Units (OUs). However, this setup can sometimes add complexity, especially for teams that support the entire organization. Consider the following example of a database operations team’s predicament. Their task […]

Migrating an Oracle application and its underlying database to the cloud can be inherently complex. Complexity is significantly amplified by various factors, including operating system compatibility, database and application version, software availability, database storage technologies such as Automatic Storage Management (ASM), and stringent business downtime requirements. AWS Application Migration Service accelerates the migration of applications to Amazon Web Services (AWS) by automatically replicating entire servers at the block level. In this post, we show you the process of migrating Oracle E-Business Suite to AWS using Application Migration Service.

Aurora Limitless is a database solution that grows and shrinks vertically and horizontally with the current workload requirements. In this post, we show you how to test performance with the common tool pgbench. This tool is used with single-node database management systems (DBMS) and is optimized for single-node use cases. As we shall see in this post, this doesn’t mean that the tool measure what we think when it comes to multi-node systems. We demonstrate how it works with Aurora Limitless. We also discuss the obstacles and opportunities you might encounter when using this tool with Aurora Limitless.

August 2025: This post was reviewed and updated for accuracy. Amazon DynamoDB is a serverless, NoSQL, fully managed database service that delivers single-digit millisecond latency at any scale. AWS recently announced the general availability of attribute-based access control (ABAC) for Amazon DynamoDB. ABAC is an authorization strategy that defines permissions based on attributes. In AWS, […]

August 2025: This post was reviewed and updated for accuracy. PostgreSQL’s ability to handle concurrent access while maintaining data consistency relies heavily on its locking mechanisms, particularly at the row level. When multiple transactions attempt to lock the same row simultaneously, PostgreSQL turns to a specialized structure called MultiXact IDs. While MultiXacts provide an efficient […]

This is the second post of a two-part series. In Part 1, we explored how you can use Automatic Data Optimization (ADO) and Oracle Information Lifecycle Management (ILM) policies for data compression. In this post, we demonstrate how to use Heat Map statistics to monitor data usage and integrate this information with hybrid partitioning and ILM data movement policies to move data to more cost-effective storage solutions.

In this two-part series, we demonstrate how to optimize storage for Oracle database workloads on AWS by using Oracle’s built-in features, such as Heat Map, Automatic Data Optimization (ADO), and hybrid partitioning. These features help classify data by its lifecycle stage and automate data management tasks to significantly reduce storage costs, while enhancing database performance, especially for growing datasets. In this post, we explore how to use ADO and Oracle ILM policies to automatically compress data based on usage patterns.