Category: Intermediate (200)

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 […]

Today, we are adding support for Valkey 8.0 on Amazon ElastiCache. ElastiCache version 8.0 for Valkey brings faster scaling for ElastiCache Serverless and memory optimizations for node-based clusters. In this post, we discuss these improvements and how you can benefit from them.

Today, Amazon RDS has announced support for MySQL version 8.4, which is the latest Long-Term Support (LTS) major version from the MySQL community. With that, Amazon RDS now supports MySQL Community Edition versions 8.0 and 8.4. In addition to the two community-supported LTS releases, Amazon RDS also offers MySQL 5.7 under RDS Extended Support, where RDS provides critical patches and bug fixes for the engine. For any of these versions, you can bring your existing MySQL code, applications, and tools to Amazon RDS. With MySQL 8.4, the MySQL community has introduced, as well as retired, multiple features, which are listed in the MySQL 8.4 reference manual. In this post, we explore some of these features, list known breaking changes, and provide recommendations to ease the migration of your workloads to this version.

Amazon Aurora Serverless v2 now supports scaling capacity down to 0 ACUs, enabling you to optimize costs during periods of database inactivity. Aurora Serverless is an on-demand, auto scaling configuration of Aurora that automatically adjusts your database capacity based on your workload requirements. Aurora Serverless measures database capacity in Aurora Capacity Units (ACUs) billed per second. 1 […]

In this post, we discuss how Coinbase migrated their user clustering system to Amazon Neptune Database, enabling them to solve complex and interconnected data challenges at scale.

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, these attributes are called tags. You can attach tags to […]

In this post, we provide a step-by-step guide to update the endpoint name for a new Amazon RDS instance while keeping the existing endpoint name, along with key considerations for this process.

Amazon DynamoDB is ideal for storing chat history and metadata due to its scalability and low latency. DynamoDB can efficiently store chat history, allowing quick access to past interactions. User-specific metadata, such as preferences and session information, can be stored to personalize responses and manage active sessions, enhancing the overall chatbot experience.In this post, we explore how to design an optimal schema for chatbots, whether you’re building a small proof of concept application or deploying a large-scale production system.

In the dynamic world of digital retail, performance, resilience, and availability are not only desirable qualities, they are essential. Recently, Dafiti, a leading fashion and lifestyle ecommerce conglomerate operating in Brazil, Argentina, Chile, and Colombia, undertook a significant transformation of its critical database infrastructure by migrating from self-managed MySQL Server 5.7 on Amazon EC2 to Amazon Aurora MySQL. This strategic move improved the resiliency and efficiency of its database operations. In this post, we show you why we chose Aurora MySQL-Compatible and how we migrated our critical database infrastructure.

In Part 1 of this post, we covered how Retrieval Augmented Generation (RAG) can be used to enhance responses in generative AI applications by combining domain-specific information with a foundation model (FM). However, we stayed focused on the semantic search aspect of the solution, assuming that our vector store was already built and fully populated. In this post, we explore how to generate vector embeddings on Wikipedia data stored in a SQL Server database hosted on Amazon RDS. We also use Amazon Bedrock to invoke the appropriate FM APIs and an Amazon SageMaker Jupyter Notebook to help us orchestrate the overall process.