Category: Amazon DynamoDB

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.

Amazon DynamoDB, Amazon Bedrock, and LangChain can provide a powerful combination for building robust, context-aware chatbots. In this post, we explore how to use LangChain with DynamoDB to manage conversation history and integrate it with Amazon Bedrock to deliver intelligent, contextually aware responses. We break down the concepts behind the DynamoDB chat connector in LangChain, discuss the advantages of this approach, and guide you through the essential steps to implement it in your own chatbot.

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 explore how to import pre-generated Amazon Personalize recommendations into Amazon DynamoDB.

As organizations increasingly rely on Amazon DynamoDB for their operational database needs, the demand for advanced data insights and enhanced search capabilities continues to grow. Leveraging the power of Amazon OpenSearch Service and Amazon Bedrock, you can now unlock generative artificial intelligence (AI) capabilities for your DynamoDB data. In this post, we show how you […]

Samsung Cloud is a cloud-based service that provides services such as backup/restore and synchronization, sharing, and device authentication of user data for all Samsung devices, including Galaxy smartphones around the world. This blog post introduces five approaches Samsung Cloud has taken to continuously lower the total cost of ownership (TCO) for Amazon DynamoDB since migrating from Apache Cassandra to DynamoDB in 2015.

Amazon Q Developer, a generative artificial intelligence (AI) assistant, can help accelerate the development of applications on AWS. In this post, we create a DynamoDB table using IaC then perform create, read, update, and delete (CRUD) operations on the table using Python and Boto3 (with additional observations for JavaScript and Java at the end of the post). We demonstrate how Amazon Q can improve your speed of development for these tasks.

Customers often ask for guidance on how to obtain the count of items in a table or within specific partitions (item collections). In this post, we explore several methods to achieve this, each tailored to different use cases, with a focus on balancing accuracy, performance, and cost.

In this post, we provide an overview of Delivery Hero’s goals and how we used Amazon DynamoDB to build a scalable and cost-efficient solution, leveraging gamification to improve restaurant operations.

Amazon DynamoDB continuously sends metrics about its behavior to Amazon CloudWatch. Something I’ve heard customers ask for is how to get a count of successful requests of each operation type (for example, how many GetItem or DeleteItem calls were made) in order to better understand usage and costs. In this post, I show you how to retrieve this metric.