# Amazon DynamoDB Amazon DynamoDB is a serverless, fully managed, distributed NoSQL database with single-digit millisecond performance at any scale. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Propagate user authorization context in AI agents with Amazon Bedrock AgentCore DevFeed: [Propagate user authorization context in AI agents with Amazon Bedrock AgentCore]() Original publisher: [Read original article]() Author: Anshu Bathla Published: 2026-08-19T17:24:15Z Content type: article Language: en Sources: [AWS Security Blog]() Topics: [Amazon Bedrock AgentCore](), [AI Agent](), [Authorization](), [AWS Identity and Access Management (IAM)](), [Amazon Bedrock Knowledge Bases](), [Amazon DynamoDB](), [Amazon S3](), [prompt injection](), [Amazon Web Services](), [Software as a service]() Tags: [access-control](), [advanced-300](), [ai-agents](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [amazon-bedrock-knowledge-bases](), [amazon-dynamodb](), [amazon-s3](), [artificial-intelligence](), [authorization](), [aws](), [prompt-injection](), [security-blog](), [security-identity-compliance](), [technical-how-to]() ### AI overview This article explains how to propagate user authorization context through AI agents built with Amazon Bedrock AgentCore. It presents a pattern for enforcing least-privilege access in downstream data services and infrastructure, so agents can access DynamoDB, Bedrock Knowledge Bases, S3-backed documents, and other sources only within the requesting user's permissions. ### Source excerpt Many teams now deploy AI agents that pull from Amazon DynamoDB tables, document repositories, software as a service (SaaS) platforms, and internal knowledge bases to answer questions and automate workflows. A key risk in these deployments is that the agent has no awareness of who's asking, so it might return data the user shouldn't see. [...] ## AWS Weekly Roundup: AWS Heroes Summit, Web Search on Amazon Bedrock, Dogwood, Kiro Crew, and more (August 10, 2026) DevFeed: [AWS Weekly Roundup: AWS Heroes Summit, Web Search on Amazon Bedrock, Dogwood, Kiro Crew, and more (August 10, 2026)]() Original publisher: [Read original article]() Author: Channy Yun (윤석찬) Published: 2026-08-10T15:45:03Z Content type: article Language: en Sources: [AWS News Blog]() Topics: [Amazon Bedrock](), [Amazon Web Services](), [Artificial Intelligence](), [Amazon Bedrock AgentCore](), [Amazon DynamoDB](), [AWS Transform](), [Embeddings](), [Containers](), [Serverless](), [Mainframe](), [OpenAI]() Tags: [ai](), [amazon](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [amazon-dynamodb](), [applications](), [aws](), [aws-lambda](), [aws-transform](), [bedrock](), [containers](), [embeddings](), [kiro](), [modernization](), [news](), [open-source](), [product](), [serverless](), [week-in-review]() ### AI overview This AWS Weekly Roundup covers the AWS Heroes Summit and selected launches, including web search for OpenAI models in Amazon Bedrock, dedicated runtime instances for agents in Amazon Bedrock AgentCore, vector search in Amazon DynamoDB, and general availability of AWS Transform continuous modernization. ### Source excerpt Last week, we brought together AWS Heroes from around the world to connect, collaborate, and celebrate the builders who go above and beyond for the AWS community. The AWS Heroes Summit, an invite-only annual gathering, brings global experts specializing in fields like AI, serverless, and containers together for direct collaboration, technical deep-dives, and feedback sessions [...] ## ScyllaDB 2026.2: DynamoDB Streams and Vector Search, Trie Indexes, and Strongly Consistent Tables DevFeed: [ScyllaDB 2026.2: DynamoDB Streams and Vector Search, Trie Indexes, and Strongly Consistent Tables]() Original publisher: [Read original article]() Author: Tzach Livyatan Published: 2026-06-29T19:18:20Z Content type: release Language: en Sources: [ScyllaDB]() Topics: [2026.2](), [Alternator (for DynamoDB)](), [API](), [Amazon DynamoDB](), [ScyllaDB Cloud](), [AI search](), [Streams](), [Replication](), [event driven](), [Latency]() Tags: [2026-2](), [amazon-dynamodb](), [api](), [event](), [event-driven](), [features](), [latency](), [performance](), [product](), [replication](), [scylladb](), [scylladb-cloud](), [search](), [streams](), [updates](), [vector]() ### AI overview ScyllaDB 2026.2 adds generally available DynamoDB Streams compatibility and a Vector Search extension to Alternator, alongside ScyllaDB Cloud integrations, Trie index performance improvements, stability updates, and experimental strongly consistent tables and online migration. ### Source excerpt ScyllaDB 2026.2 brings a combination of GA new features, exciting experimental features, and multiple stability and external use case improvements. ## Building a Stateful IT Service Desk Agent with LangGraph on Amazon EKS DevFeed: [Building a Stateful IT Service Desk Agent with LangGraph on Amazon EKS]() Original publisher: [Read original article]() Author: Sunil Ramachandra Published: 2026-06-26T14:16:08Z Content type: tutorial Language: en Sources: [AWS Open Source Blog]() Topics: [AI Agent](), [Amazon EKS](), [Kubernetes](), [Amazon DynamoDB](), [Persistence](), [FastAPI](), [OpenTelemetry](), [Orchestration](), [autoscaling](), [Graphs](), [tracing](), [Open Source]() Tags: [ai-agent](), [amazon-dynamodb](), [amazon-eks](), [amazon-elastic-kubernetes-service](), [autoscaling](), [backend](), [graphs](), [kubernetes](), [open-source](), [opentelemetry](), [orchestration](), [persistence](), [tracing]() ### AI overview This article presents a stateful IT service desk AI agent built with LangGraph. It autonomously handles routine Level 1 support requests, escalates complex issues with context, persists state in Amazon DynamoDB, and uses Amazon EKS, FastAPI, OpenTelemetry, and Kubernetes autoscaling. ### Source excerpt IT support teams face a persistent challenge: employees expect instant answers to common questions (VPN setup, single sign-on troubleshooting, new-hire onboarding), but novel or complex issues still require human expertise. An AI agent that confidently answers, "How do I reset my VPN?" but hallucinates a response to "My IAM Identity Center session keeps expiring after [...] ## Reliability lessons from the 2025 AWS DynamoDB outage DevFeed: [Reliability lessons from the 2025 AWS DynamoDB outage]() Original publisher: [Read original article]() Author: Gavin Cahill Published: 2025-11-07T00:00:00Z Content type: article Language: en Sources: [Gremlin Blog]() Topics: [Amazon DynamoDB](), [Amazon Web Services](), [Complex Systems](), [systems](), [Cloud]() Tags: [2025](), [amazon-dynamodb](), [applications](), [aws](), [cloud](), [industry](), [outage](), [outages](), [systems]() ### AI overview The article examines reliability lessons from the October 2025 AWS DynamoDB outage in US-EAST-1, which began with a DNS issue and was followed by an Amazon EC2 outage. It recommends mapping service dependencies, testing how applications behave when dependencies are unavailable, and distinguishing critical from non-critical dependencies. ### Source excerpt In October 2025, Amazon DynamoDB had a massive outage that took down hundreds of systems. Find out what your team can do to minimize the impact of similar outages in the future. ## Dynamo, DynamoDB, and Aurora DSQL DevFeed: [Dynamo, DynamoDB, and Aurora DSQL]() Original publisher: [Read original article]() Author: Marc Brooker Published: 2025-08-15T00:00:00Z Content type: article Language: en Sources: [Marc Brooker's Blog](), [Marc Brooker's Blog]() Topics: [Amazon DynamoDB](), [DSQL](), [Dynamo](), [NoSQL](), [Replication](), [Availability](), [Serverless](), [SQL](), [Amazon Web Services](), [Transactions]() Tags: [amazon-dynamodb](), [availability](), [aws](), [data](), [database](), [databases](), [dsql](), [dynamo](), [replication](), [serverless](), [sql]() ### AI overview This article compares the architectures of Amazon Dynamo, Amazon DynamoDB, and Aurora DSQL, beginning with how they provide durability and availability when a single host fails. It explains Dynamo's replication across successor nodes in a consistent-hashing ring and contrasts it with DynamoDB's replica groups, multi-AZ servers, Paxos replication, quorum acknowledgments, and scaling behavior. ### Source excerpt Dynamo, DynamoDB, and Aurora DSQL Names are hard, ok? People often ask me about the architectural relationship between Amazon Dynamo (as described in the classic 2007 SOSP paper), Amazon DynamoDB (the serverless distributed NoSQL database from AWS), and Aurora DSQL (the serverless distributed SQL database from AWS). There's a ton to say on the topic, but I'll start off on comparing how the systems achieve a few key properties. The key references for this post are: For Dynamo, Dynamo: Amazon's Highly Available Key-value Store from SOSP'07. For DynamoDB, Amazon DynamoDB: A Scalable, Predictably Performant, and Fully Managed NoSQL Database Service from ATC'22, Distributed Transactions at Scale in Amazon DynamoDB from ATC'23, and Lessons learned from 10 years of DynamoDB from the Amazon Science blog. For DSQL, my blog series on DSQL. Durability The databases we're looking at offer different levels of durability, but all three are designed not to lose data when a single host fails. Dynamo does this by taking advantage of its consistent hashing approach, replicating the data across multiple hosts in order in the hash ring: To achieve high availability and durability, Dynamo replicates its data on multiple hosts. Each data item is replicated at N hosts. ... Each key, k, is assigned to a coordinator node[]. The coordinator is in charge of the replication of the data items that fall within its range. In addition to locally storing each key within its range, the coordinator replicates these keys at the N-1 clockwise successor nodes in the ring. Like Dynamo, DynamoDB assigns a node in a hash ring to each individual item. But that's where the similarities stop. Instead of replicating across multiple nodes in the ring, in DynamoDB each node consists of a replica group with multiple servers in multiple AZs using Paxos to replicate the data. Instead of appearing the ring N times, each item appears once, and takes advantage of fault-tolerant nodes rather than spreading over multiple ## DynamoDB Transactions: An E-Commerce with Amazon DynamoDB DevFeed: [DynamoDB Transactions: An E-Commerce with Amazon DynamoDB]() Original publisher: [Read original article]() Author: Guillermo Ojeda Published: 2023-11-09T18:42:01Z Content type: tutorial Language: en Sources: [Guille Ojeda]() Topics: [Amazon DynamoDB](), [Database](), [NoSQL](), [Amazon Web Services](), [Back end]() Tags: [acid](), [amazon-dynamodb](), [amazon-web-services](), [architecture](), [aws](), [backend](), [customers](), [database](), [databases](), [dynamodb](), [e-commerce](), [nosql](), [operations](), [products]() ### AI overview This tutorial explains how to use DynamoDB transactions in an e-commerce application to atomically verify product stock, decrement inventory, and create an order. It describes the race condition that can occur when separate read and write operations process simultaneous purchases. ### Source excerpt We're building an e-commerce app with DynamoDB for the database, pretty similar to the one we built for the DynamoDB Database Design article. No need to go read that issue (though I think it came up great), here's how our database works: Customers a... ## The DynamoDB paper DevFeed: [The DynamoDB paper]() Original publisher: [Read original article]() Author: Marc Brooker Published: 2022-07-12T00:00:00Z Content type: article Language: en Sources: [Marc Brooker's Blog](), [Marc Brooker's Blog]() Topics: [Amazon DynamoDB](), [NoSQL](), [Database](), [systems](), [API](), [Dynamo](), [Amazon Web Services]() Tags: [amazon-dynamodb](), [api](), [architecture](), [aws](), [customers](), [database](), [dynamo](), [performance](), [scale](), [systems]() ### AI overview The article discusses the DynamoDB paper, highlighting the database service's massive scale, evolution to meet customer needs, and lessons from real-world distributed-system design. It explains how non-uniform workloads caused partition hot spots and throttling despite sufficient total provisioned throughput, and contrasts DynamoDB's architecture with the earlier Dynamo system. ### Source excerpt The DynamoDB paper The other database called Dynamo This week at USENIX ATC'22, a group of my colleagues1 from the AWS DynamoDB team are going to be presenting their paper Amazon DynamoDB: A Scalable, Predictably Performant, and Fully Managed NoSQL Database Service. This paper is a rare look at a real-world distributed system that runs at massive scale. From the paper: In 2021, during the 66-hour Amazon Prime Day shopping event, Amazon systems ... made trillions of API calls to DynamoDB, peaking at 89.2 million requests per second 89 million requests per second is a big database by any standards (and that's just Amazon's use of DynamoDB)! What's exciting for me about this paper is that it covers DynamoDB's journey, and how it has changed over time to meet customers' needs. There are relatively few papers that cover this kind of change over time. For example: The uniform distribution of throughput across partitions is based on the assumptions that an application uniformly accesses keys in a table and the splitting a partition for size equally splits the performance. However, we discovered that application workloads frequently have non-uniform access patterns both over time and over key ranges. When the request rate within a table is non-uniform, splitting a partition and dividing performance allocation proportionately can result in the hot portion of the partition having less available performance than it did before the split. Since throughput was allocated statically and enforced at a partition level, these non- uniform workloads occasionally resulted in an application's reads and writes being rejected, called throttling, even though the total provisioned throughput of the table was sufficient to meet its needs. This is the kind of assumption in a system design--that splitting makes performance better--that's really easy to overlook when designing a system, and potentially difficult to fix when you're in production. A lot of what makes systems like DynamoDB so useful is ## Security Auditing Amazon RDS Database Activity with Teleport DevFeed: [Security Auditing Amazon RDS Database Activity with Teleport]() Original publisher: [Read original article]() Author: info@goteleport.com (Janakiram MSV) Published: 2022-06-08T00:00:00Z Content type: tutorial Language: en Sources: [Teleport]() Topics: [Amazon RDS](), [audit](), [Security](), [Amazon Web Services](), [Amazon DynamoDB](), [Amazon EC2](), [VPC]() Tags: [amazon-dynamodb](), [amazon-ec2](), [amazon-rds](), [audit](), [aws](), [bastion-host](), [cloudtrail](), [security](), [vpc]() ### AI overview This tutorial explains how to use Teleport to record and audit Amazon RDS database access events, including live session monitoring, audit logs for DDL and DML queries, and event storage in Amazon DynamoDB. ### Source excerpt This blog post explains how to record and audit Amazon RDS Database activities with Teleport. ## Espressif's AWS-Based AIoT Platform and ESP RainMaker Services DevFeed: [Espressif's AWS-Based AIoT Platform and ESP RainMaker Services]() Original publisher: [Read original article]() Author: John Lee Published: 2022-05-30T00:00:00Z Content type: article Language: en Sources: [Blog on Developer Portal]() Topics: [Espressif](), [Esp Rainmaker](), [Amazon Web Services](), [Internet of things](), [ESP32](), [Serverless](), [AWS Lambda](), [Amazon DynamoDB](), [cloud-infrastructure]() Tags: [amazon-dynamodb](), [aws](), [aws-lambda](), [blog](), [esp-rainmaker](), [esp32](), [espressif](), [hardware](), [instagram](), [iot](), [newsletter](), [sdks](), [serverless]() ### AI overview Espressif describes its AWS partnership and ESP RainMaker, an AIoT solution covering chips, device firmware, voice-assistant integration, mobile apps, and dashboards. The article states that ESP RainMaker uses AWS serverless architecture, including AWS Lambda and Amazon DynamoDB. ### Source excerpt Hi everyone, By heavily investing in R&D, we have developed various series of robust, secure, wireless-communication MCUs which today power more than 700 million devices worldwide. Thus, we have established Espressif as a unique, one-stop, AIoT solution-provider of not just powerful hardware, but also of innovative SDKs, Firmware, Cloud backend, phone apps, and voice integrations.