Category: Intermediate (200)

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.

In this series of posts, we dive deep into performance testing of MySQL environments being migrated from on-premises to AWS. In this post, we review two different approaches to testing migrated environments with traffic that is representative of real production traffic: capturing and replaying traffic using a playback application, and mirroring traffic as it comes in using a proxy. This means you’re validating your environment using realistic data access patterns.

Amazon RDS provides different instance types optimized to fit different relational database use cases. You can modify provisioned instances manually from the Amazon RDS console or using an API. When modifications need to be done on a recurring basis, such as scaling an instance up and down during predefined periods of time, you can automate the task using EventBridge Scheduler and Lambda. In this post, we present a solution using Amazon EventBridge Scheduler and AWS Lambda that allows you to schedule a programmatic modification of a DB instance with specific tags.

Amazon DocumentDB now supports authentication of database users using IAM – users and applications can authenticate to Amazon DocumentDB clusters using IAM users and roles. In this post, we discuss this new feature and provide you resources on how to enable IAM authentication in your Amazon DocumentDB cluster.

Whether you’ve used DynamoDB for a day or a decade, this question has no practical relevance. As a serverless database, DynamoDB doesn’t have a version. DynamoDB has had no version upgrades, no maintenance windows, no patching, and no downtime due to maintenance since launching in January 2012. You access new DynamoDB features as they become […]

In this post, we discuss the challenges with traditional data analytics mechanisms, our approach to solve them, and how you can use Amazon Aurora PostgreSQL-Compatible Edition zero-ETL integration with Amazon Redshift, which is generally available as of October 15th, 2024.

This post is a follow-up to How Zendesk tripled performance by moving a legacy system onto Amazon Aurora and Amazon Redshift. In this post, we go over the techniques we used to plan and upgrade major versions of Aurora PostgreSQL databases for Zendesk Explore with minimal customer downtime. We also discuss the performance optimizations we performed, the cost savings we achieved, and how we accomplished all of this within a period of 6 months. AWS Technical Account Managers played a significant role in helping us achieve these goals in a short period of time. The upgrade was performed successfully and without customer downtime.