# Amazon DynamoDB Published articles for Amazon DynamoDB. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Run DuckDB analytics on your Amazon DynamoDB data with zero-ETL DevFeed: [Run DuckDB analytics on your Amazon DynamoDB data with zero-ETL]() Original publisher: [Read original article]() Author: Lee Hannigan Published: 2026-09-11T14:53:57Z Content type: tutorial Language: en Sources: [AWS Database Blog]() Topics: [Databases](), [Amazon S3](), [Apache Iceberg](), [AWS Lambda](), [AWS IAM](), [Latency]() Tags: [advanced-300](), [amazon-dynamodb](), [amazon-s3-tables](), [analytics](), [apache-iceberg](), [aws-lambda](), [duckdb](), [how-to](), [iam](), [integration](), [sql](), [technical-how-to]() ### AI overview This tutorial explains how to run ad hoc SQL analytics on Amazon DynamoDB data with DuckDB through a zero-ETL replication flow. ### Source excerpt Run ad hoc SQL analytics on your Amazon DynamoDB data with DuckDB. A zero-ETL integration replicates your table into Apache Iceberg tables on Amazon S3 Tables, and an AWS Lambda function running DuckDB serves SQL queries through an IAM-authorized function URL. ## Building resilient real-time streaming workers with Amazon DynamoDB leases DevFeed: [Building resilient real-time streaming workers with Amazon DynamoDB leases]() Original publisher: [Read original article]() Author: Siddhesh Tiwari Published: 2026-09-10T16:14:22Z Content type: tutorial Language: en Sources: [AWS Architecture Blog]() Topics: [WebSocket](), [Amazon EC2](), [Amazon Elastic Kubernetes Service]() Tags: [advanced-300](), [amazon-dynamodb](), [amazon-ec2](), [amazon-eks](), [amazon-elastic-container-service](), [aws-fargate](), [how-to](), [real-time](), [streaming](), [technical-how-to](), [workers]() ### AI overview A tutorial for building resilient real-time WebSocket workers with Amazon DynamoDB leases. It covers conditional-write ownership, orphan reconciliation for automatic failover, and graceful shutdown to reduce deployment downtime on Amazon ECS and AWS Fargate. ### Source excerpt Real-time streaming workers that hold hundreds of persistent WebSocket connections lose data when a worker fails. Learn how to build a WebSocket fleet management system on Amazon ECS and AWS Fargate that uses Amazon DynamoDB conditional writes as a distributed lease to track ownership, fail over automatically, and deploy with low downtime. ## Amazon SageMaker Feature Store introduces UpdateRecord for feature-level writes DevFeed: [Amazon SageMaker Feature Store introduces UpdateRecord for feature-level writes]() Original publisher: [Read original article]() Author: Mona Mona Published: 2026-09-08T18:29:15Z Content type: release Language: en Sources: [Artificial Intelligence]() Topics: [AI, ML & Data Engineering](), [Training AI Models](), [AWS IAM]() Tags: [amazon-dynamodb](), [amazon-elasticache](), [amazon-machine-learning](), [amazon-sagemaker](), [amazon-sagemaker-ai](), [announcements](), [api](), [artificial-intelligence](), [aws-identity-and-access-management-iam](), [intermediate-200](), [technical-how-to]() ### AI overview Amazon SageMaker Feature Store adds the UpdateRecord API for atomic feature-level updates without reading or rewriting an entire record. ### Source excerpt Amazon SageMaker Feature Store now supports feature-level writes. With the new UpdateRecord API, you can update one or more feature values in a single call without reading or rewriting the entire record. It is available for both the Standard (Amazon DynamoDB) and In-Memory (Amazon ElastiCache) online store tiers. ## Introducing strands-dynamodb-storage: Durable agent storage for the Strands Agents SDK DevFeed: [Introducing strands-dynamodb-storage: Durable agent storage for the Strands Agents SDK]() Original publisher: [Read original article]() Author: Lee Hannigan Published: 2026-09-02T15:48:06Z Content type: release Language: en Sources: [AWS Database Blog]() Topics: [AI Development](), [Database](), [SDKs](), [Amazon Bedrock](), [AWS Lambda]() Tags: [advanced-300](), [ai-agents](), [amazon-dynamodb](), [announcements](), [memory](), [open-source](), [python](), [storage](), [strands-agents](), [typescript](), [vector]() ### AI overview Amazon Web Services announces strands-dynamodb-storage, an open-source DynamoDB backend for the Strands Agents SDK. It stores agent session state, long-term memories, and transcripts in one DynamoDB table and supports semantic memory recall through a vector index. ### Source excerpt Announcing strands-dynamodb-storage, an open source Amazon DynamoDB storage backend for the Strands Agents SDK. Back a Strands agent's session state, long-term memories, and transcripts with one DynamoDB table in your own account, and give the agent semantic recall with a vector index on that same table. ## Hybrid cloud orchestration: Modernizing on-premises infrastructure management with AWS DevFeed: [Hybrid cloud orchestration: Modernizing on-premises infrastructure management with AWS]() Original publisher: [Read original article]() Author: Sandeep Singh Published: 2026-09-01T14:01:10Z Content type: tutorial Language: en Sources: [AWS Architecture Blog]() Topics: [Orchestration](), [Kubernetes](), [Amazon EKS](), [AWS Lambda](), [Server](), [Hardware]() Tags: [amazon-dynamodb](), [amazon-eks](), [aws](), [aws-lambda](), [aws-step-functions](), [data-centers](), [deployment](), [event-driven](), [hardware](), [kubernetes](), [observability](), [orchestration](), [server](), [serverless](), [thought-leadership]() ### AI overview Tutorial on designing an AWS-based hybrid cloud orchestration system for centralized lifecycle management of distributed on-premises servers and EKS Anywhere clusters. ### Source excerpt Learn how to build a hybrid cloud orchestration solution that manages distributed on-premises infrastructure at scale using AWS serverless technologies and Amazon EKS Anywhere. Part 1 covers the core event-driven architecture patterns for automating server lifecycle and cluster management across hundreds of sites. ## How Channel Corporation modernized their architecture with Amazon DynamoDB, Part 3: User and Badge DevFeed: [How Channel Corporation modernized their architecture with Amazon DynamoDB, Part 3: User and Badge]() Original publisher: [Read original article]() Author: Haibin (Binu) Lee Published: 2026-08-25T18:39:51Z Content type: article Language: en Sources: [AWS Database Blog]() Topics: [data](), [Amazon Web Services](), [migration](), [Amazon S3](), [Latency](), [App]() Tags: [advanced-300](), [amazon](), [amazon-dynamodb](), [app](), [architecture](), [aws](), [customer-solutions](), [data](), [latency](), [migration]() ### AI overview Channel Corporation describes how it addressed DynamoDB User table throttling by separating role-specific data, including Badge data, from an all-purpose User table. The article explains the effects of bursty badge writes, item-size-based write capacity consumption, and transaction processing, and introduces an online migration approach using DynamoDB Export and Import with Amazon S3 and AWS Glue. ### Source excerpt Channel Corporation shares how they split their all-purpose Amazon DynamoDB User table into role-specific tables, moving Badge data into a dedicated UserBadge table to stop transaction-conflict throttling and GSI back pressure, and how they ran a zero-downtime online migration using DynamoDB Export and Import with Amazon S3 and AWS Glue. ## Build a unified AI agent architecture with DynamoDB and Bedrock DevFeed: [Build a unified AI agent architecture with DynamoDB and Bedrock]() Original publisher: [Read original article]() Author: Dhananjay Karanjkar Published: 2026-08-21T18:19:23Z Content type: tutorial Language: en Sources: [AWS Architecture Blog]() Topics: [AI Bots]() Tags: [advanced-300](), [agent](), [agents](), [ai](), [ai-agent](), [amazon-bedrock](), [amazon-dynamodb](), [amazon-opensearch-service](), [ann](), [api](), [architecture](), [aws-lambda](), [data-architecture](), [database](), [embeddings](), [how-to](), [retrieval](), [search](), [streams](), [sync](), [vector]() ### AI overview A tutorial for building an Amazon Bedrock agent that uses one DynamoDB table for structured data and semantic vector search. DynamoDB Streams generates embeddings when content changes, keeping the vector index synchronized. ### Source excerpt With native vector search in Amazon DynamoDB, you can store vector embeddings alongside your operational data in a single table. This post shows how to build a unified AI agent architecture where an Amazon Bedrock agent uses one DynamoDB table for both structured lookups and semantic search, with a DynamoDB Streams pipeline that keeps embeddings in sync. ## 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. [...] ## Consistency is the new latency: AI at the data layer DevFeed: [Consistency is the new latency: AI at the data layer]() Original publisher: [Read original article]() Author: Suman Chatterjee Published: 2026-08-18T11:13:20Z Content type: article Language: en Sources: [AWS Architecture Blog]() Topics: [AI Bots](), [Language models](), [web applications]() Tags: [advanced-300](), [agents](), [ai](), [amazon-aurora](), [amazon-dynamodb](), [best-practices](), [data](), [latency](), [rag](), [replication]() ### AI overview The article argues that autonomous AI agents need consistent, current data to make reliable decisions. It explains how replication lag can cause stale reads in RAG-based agent systems and points to selecting replication models that match each workload's consistency needs. ### Source excerpt As AI agents move from chatbots to taking action, their reliability depends on the consistency of the data layer beneath them. This post examines how replication lag poisons an agent's context and shows how to match Amazon Aurora, Amazon DynamoDB, and Amazon Keyspaces replication models to each task's consistency requirements. ## 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 [...] ## Amazon DynamoDB now supports real-time vector search at any scale DevFeed: [Amazon DynamoDB now supports real-time vector search at any scale]() Original publisher: [Read original article]() Author: Esra Kayabali Published: 2026-08-05T14:45:10Z Content type: release Language: en Sources: [AWS News Blog]() Topics: [AI search](), [AI, ML & Data Engineering](), [Retrieval Augmented Generation (RAG)]() Tags: [amazon-bedrock](), [amazon-dynamodb](), [announcements](), [api](), [artificial-intelligence](), [database](), [embeddings](), [generative-ai](), [latency](), [launch](), [news](), [openai](), [real-time](), [retrieval-augmented-generation](), [scale](), [search](), [serverless](), [vector]() ### AI overview Amazon DynamoDB has added generally available native vector search, allowing embeddings and operational data to be stored and queried together. The feature provides similarity-ranked results with configurable dimensions, distance functions, and filters, without a separate vector database or synchronization pipeline. ### Source excerpt DynamoDB now supports native vector search with single-digit millisecond latency at 99%+ recall. It is designed for any scale, even trillions of vectors and requires zero infrastructure management. ## AWS Weekly Roundup: One-click Lambda setup prompt, OpenAI GPT-5.6 models on Bedrock, and more (July 20, 2026) DevFeed: [AWS Weekly Roundup: One-click Lambda setup prompt, OpenAI GPT-5.6 models on Bedrock, and more (July 20, 2026)]() Original publisher: [Read original article]() Author: Channy Yun (윤석찬) Published: 2026-07-20T16:37:34Z Content type: news Language: en Sources: [AWS News Blog]() Topics: [AI-assisted coding](), [Amazon S3](), [cursor](), [GitHub Copilot]() Tags: [ai-agent](), [amazon-bedrock](), [amazon-cognito](), [amazon-dynamodb](), [amazon-s3](), [amazon-simple-storage-service-s3](), [aws](), [aws-lambda](), [kiro](), [mcp](), [news](), [openai](), [serverless](), [strands-agents](), [week-in-review]() ### AI overview AWS weekly roundup covering a one-click Lambda prompt for configuring coding agents with serverless skills and an MCP server, OpenAI GPT-5.6 models on Amazon Bedrock, and same-day Amazon S3 storage-class transitions. ### Source excerpt Last week, my team visited Seoul to meet AWS Korea User Group (AWSKRUG) leaders. AWSKRUG is the largest cloud developer community in Korea, with 20 meetup groups organized by topic and area that collectively host over 100 events each year, primarily in Seoul. My team regularly visits countries across the Asia-Pacific region, listens to feedback [...] ## 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 [...] ## Deploying cloud-based engineering workbenches with the Virtual Engineering Workbench on AWS DevFeed: [Deploying cloud-based engineering workbenches with the Virtual Engineering Workbench on AWS]() Original publisher: [Read original article]() Author: Andrea Meroni Published: 2026-04-21T21:38:49Z Content type: tutorial Language: en Sources: [AWS Open Source Blog]() Topics: [Deployment](), [Embedded Software Dev](), [Amazon API Gateway](), [Domain-driven design (DDD)](), [Authorization]() Tags: [amazon-api-gateway](), [amazon-dynamodb](), [amazon-eventbridge](), [aws](), [aws-lambda](), [cloud](), [deployment](), [design-patterns](), [embedded](), [open-source](), [react](), [serverless]() ### AI overview A tutorial for deploying the open-source Virtual Engineering Workbench on AWS. It describes a self-service platform for publishing and provisioning preconfigured development environments, its serverless architecture, and creating and launching a workbench. ### Source excerpt Introduction Organizations in automotive, manufacturing, and embedded systems increasingly rely on cloud-based development environments to reduce hardware dependency and accelerate software delivery. As vehicle architectures shift toward software-defined platforms, engineering teams need access to specialized toolchains, virtual hardware models, and simulation environments that are consistent across sites and reproducible across projects. Provisioning these environments manually [...] ## 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. ## How to Audit SSH Sessions in Amazon EC2 DevFeed: [How to Audit SSH Sessions in Amazon EC2]() Original publisher: [Read original article]() Author: info@goteleport.com (Janakiram MSV) Published: 2022-04-19T00:00:00Z Content type: tutorial Language: en Sources: [Teleport]() Topics: [Amazon EC2](), [ssh](), [Tutorial](), [audit](), [Security](), [Amazon S3](), [DynamoDB](), [OpenSSH]() Tags: [amazon-cloudwatch](), [amazon-dynamodb](), [amazon-s3](), [audit](), [aws](), [bastion](), [bastion-host](), [cloudtrail](), [compliance](), [dynamodb](), [ec2](), [how-to](), [infrastructure](), [review](), [s3](), [security](), [security-events](), [ssh](), [storage](), [tutorial]() ### AI overview A tutorial on configuring Teleport to record and audit SSH sessions on Amazon EC2 Linux instances, including centralized session playback and storing Teleport events and recordings in Amazon DynamoDB and Amazon S3. ### Source excerpt This post is a tutorial to set up security session recording for EC2 SSH access and store logs in AWS S3 and DynamoDB. ## Amazon DynamoDB Auto Scaling DevFeed: [Amazon DynamoDB Auto Scaling]() Original publisher: [Read original article]() Author: umer Published: 2017-07-29T00:00:00Z Content type: article Language: en Sources: [Code Ahoy - Articles]() Topics: [DynamoDB](), [amazon](), [Provisioning](), [Availability]() Tags: [amazon](), [amazon-dynamodb](), [availability](), [dynamodb](), [high-availability](), [pricing](), [provisioning]() ### AI overview The article explains how Amazon DynamoDB Auto Scaling adjusts provisioned read and write capacity for tables and global secondary indexes in response to traffic. It contrasts automatic scaling with fixed provisioning, describing how scaling can help address throttling during demand spikes and reduce payment for unused capacity during lower traffic. ### Source excerpt Amazon DynamoDB supports Auto Scaling which is a fantastic feature. When enabled, Auto Scaling adjusts read and write capacities of DynamoDB tables (and global secondary indexes) automatically based on the demand. If you haven't used DynamoDB before, you might be wondering why is this important? Before Auto Scaling, the users were required to provide fixed capacities for their tables. These capacities were static and didn't respond to traffic demands. This was problematic because: The application performance and high-availability was compromised whenever the utilization exceeded the provisioned throughput. When this happened, DynamoDB throttled requests, which resulted in loss of data or poor user experience. Cost control was poor at best. DynamoDB charges you by how much you provision. You end up paying the full cost by provisioned amounts, even if you use less than what you provisioned. In other words, if you overprovision for peak load, you'll pay extra during the non-peak hours, when the capacity isn't being full utilized. On the other hand, if you underprovision, the performance of your application will suffer due to throttling when the load exceeds the provisioned capacity. Many real-world use cases are difficult to predict in advance and fluctuations are common. Speaking of traffic fluctuations, they are prevalent and hard to deal with. In mobile gaming, the traffic can increase suddenly if Apple or Google features the game, or the publisher runs a massive ad campaign. A website can suddenly see a large number of visitors if an article is picked up by a major newspaper or news aggregator site. What is DynamoDB Auto Scaling? Historical Perspective Auto Scale Target Utilization DynamoDB Auto Scaling Pricing DynamoDB Auto Scaling vs On Demand When to use Auto Scaling vs On Demand? DynamoDB and DAX What is DynamoDB Auto Scaling? DynamoDB Auto Scaling feature lets you automatically manage throughput in response to your traffic patterns without throttling your user