# DynamoDB Published articles for 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. ## Perplexity's AI agents helped build a database. They weren't allowed to run it. DevFeed: [Perplexity's AI agents helped build a database. They weren't allowed to run it.]() Original publisher: [Read original article]() Author: Amanda Caswell Published: 2026-09-16T21:51:15Z Content type: article Language: en Sources: [The New Stack]() Topics: [Database](), [DynamoDB](), [Rust](), [Artificial Intelligence](), [rocksdb](), [Amazon S3]() Tags: [agents](), [ai](), [ai-agents](), [ai-infrastructure](), [api](), [coding](), [coding-agents](), [database](), [databases](), [dynamodb](), [open-source](), [performance](), [perplexity](), [rocksdb](), [rust](), [s3]() ### AI overview Perplexity built CobbleDB, a Rust key-value store, after finding DynamoDB too costly and insufficiently controllable for its search workload. Coding agents helped develop it, but were not allowed to run it in production. Perplexity measured lower read latency and expects lower costs, with plans to open-source the database. ### Source excerpt Perplexity decided it was paying too much for DynamoDB and wasn't getting the control it wanted over read performance. So The post Perplexity's AI agents helped build a database. They weren't allowed to run it. appeared first on The New Stack. ## The Original Serverless Architecture is Still Here DevFeed: [The Original Serverless Architecture is Still Here]() Original publisher: [Read original article]() Author: Khan Academy Published: 2018-05-31T22:00:00Z Content type: opinion Language: en Sources: [Khan Academy]() Topics: [serverless architecture](), [Kubernetes](), [Orchestration](), [Docker](), [Cloud](), [Firebase](), [DynamoDB]() Tags: [amazon](), [cloud](), [containers](), [docker](), [dynamodb](), [engineering](), [firebase](), [infrastructure](), [kubernetes](), [news](), [orchestration](), [serverless](), [serverless-architecture]() ### AI overview This commentary compares Kubernetes-based architectures with serverless approaches. It explains that Kubernetes offers flexibility through containers, Helm, ingress controllers, monitoring tools, and service meshes, but requires substantial configuration and maintenance. Serverless platforms such as Firebase and Amazon Lambda abstract away server infrastructure so developers can focus on applications and stateless functions. ### Source excerpt By Kevin Dangoor This month, my colleague Dave Rosile and I went to GlueCon 2018 in sunny Denver, ... Read more ## Terraform Scalability: When IaC Outgrows Your Setup DevFeed: [Terraform Scalability: When IaC Outgrows Your Setup]() Original publisher: [Read original article]() Author: Mrinalini Sugosh Published: 2026-08-10T00:00:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [Terraform](), [Infrastructure as code](), [Scalability](), [iac-security]() Tags: [dynamodb](), [harness](), [iac](), [s3](), [scalability](), [security](), [terraform]() ### AI overview This article examines how Terraform scalability challenges emerge as infrastructure code and teams grow. It discusses bottlenecks involving state management, collaboration, governance, large state files, locking, plan times, module sprawl, and version drift, and introduces Harness IaCM as a management solution. ### Source excerpt Terraform scalability issues slow teams down. Learn how to overcome IaC bottlenecks with better management. See how Harness helps. | Blog ## Cloud provider postmortems: volume vs depth DevFeed: [Cloud provider postmortems: volume vs depth]() Original publisher: [Read original article]() Author: Sridhar Rajarao Published: 2026-08-05T00:00:00Z Content type: opinion Language: en Sources: [Sridhar Rajarao]() Topics: [Cloud](), [incident](), [SRE](), [site-reliability-engineering](), [engineering-culture]() Tags: [2017](), [2025](), [2026](), [aws](), [azure](), [cloud](), [dynamodb](), [engineering-culture](), [gcp](), [incident](), [incident-management](), [postmortems](), [s3](), [sre](), [transparency](), [writeup]() ### AI overview The article compares public postmortem practices among Google Cloud, Azure, and AWS. It argues that Google Cloud emphasizes high volume and speed, Azure emphasizes detailed transparency and customer accountability, and AWS publishes fewer writeups with greater depth and industry influence. ### Source excerpt GCP publishes 100+ postmortems a year. AWS publishes almost none. Azure has become the transparency leader. What each posture reveals about engineering culture, and what SREs should steal from all three. ## Migrate CloudFormation to Pulumi with Discovered Stacks DevFeed: [Migrate CloudFormation to Pulumi with Discovered Stacks]() Original publisher: [Read original article]() Author: Alejandro Cotroneo Published: 2026-07-30T00:00:00Z Content type: tutorial Language: en Sources: [Pulumi]() Topics: [Infrastructure as code](), [AWS CloudFormation](), [migration](), [Amazon Web Services](), [VPC](), [Amazon S3](), [DynamoDB](), [AWS IAM]() Tags: [aws](), [cloudformation](), [dynamodb](), [git](), [iac](), [iam](), [import](), [insights](), [kms](), [migration](), [pulumi](), [pulumi-cloud](), [pulumi-neo](), [s3](), [tutorials](), [vpc]() ### AI overview A tutorial shows how to migrate a 61-resource AWS CloudFormation stack to Pulumi using Discovered Stacks in Pulumi Cloud. It explains how to find the stack, review migration status, start the migration, resolve unmatched resources, and verify the result with a zero-diff pulumi preview. ### Source excerpt With Discovered Stacks, Pulumi Cloud does the bookkeeping for a CloudFormation migration: every resource in the stack gets an explicit migration status, and the migration is done when the code provably matches the cloud. In this tutorial, we take one real CloudFormation stack from discovered to migrated and managed by Pulumi IaC, end to end. What we're migrating Our example is payments-api, a CloudFormation stack with 61 resources: a VPC, an Aurora ledger database behind an RDS Proxy, an assets S3 bucket, a DynamoDB ledger table, a Kinesis payment-events pipeline, and the IAM roles, KMS keys, and secrets that wire them together. The plan has five steps: Find the stack in Pulumi Cloud. Review the migration breakdown at a glance. Start the migration. Resolve the stragglers, so every resource is accounted for. Confirm the quality gate: a zero-diff pulumi preview. We'll use Pulumi Neo to do the heavy lifting, but nothing here depends on it. The same flow works with your own coding agent or entirely by hand, because migration status is derived from actual stack state -- however the work gets done, the console shows the same progress. Step 1: Find your stack in Pulumi Cloud Discovered Stacks builds on Discovery, so the only prerequisite is a scanned cloud account -- the AWS account holding your CloudFormation stacks. Once a scan has run, open the Stacks page and turn on Show Discovered Stacks. Your CloudFormation stacks appear alongside your Pulumi stacks. The project name comes from the CloudFormation stack (payments-api), and the stack name encodes the account and region it came from, so the same template deployed to two regions shows up as two distinct discovered stacks. Step 2: Plan the migration at a glance Open the discovered stack's Migration tab. It lays out all 61 resources of payments-api by status, so you can visualize the migration before touching anything: 54 Ready: mapped to a Pulumi type and confirmed to exist -- importable right now. 2 Not found: mapped, but ## Treating Pricing Changes Like Code Deploys DevFeed: [Treating Pricing Changes Like Code Deploys]() Original publisher: [Read original article]() Author: Ananda Kanagaraj Sankar Published: 2026-07-22T18:09:48Z Content type: article Language: en Sources: [Thumbtack Engineering - Medium]() Topics: [Deployment](), [DynamoDB](), [Go Language](), [Code review]() Tags: [code-review](), [deployment](), [devops](), [dry-run](), [dynamodb](), [engineering](), [go](), [marketplaces](), [monitor](), [platform-engineering](), [pricing-strategy]() ### AI overview This article explains how Thumbtack handled large pricing changes by comparing them with code deployments. Pricing bids stored in DynamoDB were rewritten in bulk using custom Go scripts, but those scripts lacked built-in resumability, runtime control, and durable records for undoing changes. ### Source excerpt Making large pricing changes safe to run, safe to resume, and safe to undo. Most days, shipping code to production at Thumbtack is a non-event. You merge your change, a deployment pipeline takes it from there and it rolls out while you monitor the rollout. If a deploy stalls, the system already knows which step it stalled on. If it goes wrong, rolling back is a button away. It's the return on years of platform work, so routine now that we mostly forget it's there. Which is the point: Safety is built into the road, not into how carefully each person drives. Changing pricing data never had that platform at Thumbtack. Pricing is how a marketplace like ours sustains itself. It lets us capture a share of the value we help create, and it's something we tune on an ongoing basis rather than set once. We test pricing changes, measure their impact and roll out the ones that work. Mechanically, this runs on an auction. When a customer creates a project on Thumbtack, pros compete for the lead, and what a pro is willing to pay shows up as a bid. Those bids are stored per pro's service offering (a specific offering a pro provides, like house cleaning or landscaping) as bid settings in DynamoDB. Rolling out a pricing change means rewriting those settings in bulk, often a few hundred thousand of them at a time, several times a year. For a long time, each of the pricing rollouts was a custom Go script written for that specific change. The scripts were production grade. They went through code review, ran in staging first, did a dry run before any live write, and were rolled out in stages. That was a reasonable way to work: the changes are infrequent, each one is genuinely different from the last, and a script you can read top to bottom is easy to trust. But what reviews and a good runbook can't give you is the runtime machinery. A script doesn't remember where it was when it died, so it can't safely pick up where it left off. It has no real sense of how hard it's pushing the database ## The DynamoDB Connector is now in Tinybird Forward DevFeed: [The DynamoDB Connector is now in Tinybird Forward]() Original publisher: [Read original article]() Author: Tomás Healy Published: 2026-07-16T11:00:00Z Content type: release Language: en Sources: [Tinybird]() Topics: [DynamoDB]() Tags: [dynamodb](), [product-updates]() ### AI overview Tinybird Forward now supports mirroring DynamoDB tables using a .connection file and a single deploy. The change addresses a migration gap for Tinybird Classic users. ### Source excerpt Mirror your DynamoDB tables to Tinybird Forward with a .connection file and a single deploy. For Classic users, this closes the last gap to migrating. ## How Order Editing Replaced a Failing DynamoDB Pipeline with Tinybird DevFeed: [How Order Editing Replaced a Failing DynamoDB Pipeline with Tinybird]() Original publisher: [Read original article]() Author: Tinybird Published: 2026-02-24T11:00:00Z Content type: article Language: en Sources: [Tinybird]() Topics: [Shopify](), [DynamoDB]() Tags: [customer-stories](), [debugging](), [dynamodb](), [infrastructure](), [pipeline](), [shopify](), [team]() ### AI overview A three-person team handling $5B in Shopify GMV replaced a failing DynamoDB pipeline with Tinybird, reducing the need to debug reconciliation failures and allowing the team to focus on shipping features. ### Source excerpt A three-person team running $5B in Shopify GMV couldn't afford to babysit analytics infrastructure. Here's how they stopped debugging reconciliation failures and started shipping features. ## HubSpot Incident Report for October 20, 2025 DevFeed: [HubSpot Incident Report for October 20, 2025]() Original publisher: [Read original article]() Author: Kartik Vishwanath Published: 2025-11-11T17:09:36Z Content type: news Language: en Sources: [HubSpot]() Topics: [incident](), [Amazon Web Services](), [Cloud](), [Resilience](), [Amazon Simple Queue Service (SQS)](), [Amazon EC2](), [AWS IAM](), [DynamoDB]() Tags: [amazon](), [amazon-sqs](), [analysis](), [aws](), [cloud](), [data](), [database](), [distributed](), [dynamodb](), [ec2](), [engineering](), [failover](), [hubspot](), [iam](), [improvements](), [incident](), [message-queue](), [outage](), [recovery](), [report](), [resilience]() ### AI overview HubSpot reports that a severe AWS outage in the us-east-1 region on October 20, 2025 disrupted multiple product features and affected third-party vendors. The incident caused failures involving DynamoDB, IAM, SQS, and EC2, and degraded HubSpot's TQ2 background task processing. HubSpot says it completed an analysis and is implementing resilience improvements. ### Source excerpt On October 20, 2025, HubSpot experienced a significant service disruption affecting multiple product features due to a severe AWS outage in the us-east-1 region. While our infrastructure remained intact, the widespread nature of the cloud provider failure impacted both our services and critical third-party vendors we rely on. We've completed a thorough analysis of this incident and are implementing comprehensive improvements to strengthen our resilience against future cloud provider disruptions. ## Durable under pressure: How developers kept running during the AWS us-east-1 outage DevFeed: [Durable under pressure: How developers kept running during the AWS us-east-1 outage]() Original publisher: [Read original article]() Author: Luke Knepper Published: 2025-11-07T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [Amazon Web Services](), [Availability](), [Cloud](), [Disaster Recovery](), [Replication](), [incident](), [DynamoDB]() Tags: [2025](), [availability](), [aws](), [disaster-recovery](), [dynamodb](), [high-availability](), [incident](), [outage](), [temporal-concepts](), [us]() ### AI overview The article examines the October 20, 2025 AWS us-east-1 outage and explains how multi-region high availability and disaster recovery strategies helped some applications continue running. It describes Temporal Cloud's replication and failover features and FireHydrant's preparation and testing. ### Source excerpt The AWS us-east-1 outage affected many, but some devs were able to stay afloat. Find out how. ## Engineering stories behind the Medium Daily Digest Algorithm: Part 2 DevFeed: [Engineering stories behind the Medium Daily Digest Algorithm: Part 2]() Original publisher: [Read original article]() Author: Raphael Montaud Published: 2025-08-28T11:31:36Z Content type: article Language: en Sources: [Medium]() Topics: [Algorithm](), [migration](), [Databases]() Tags: [algorithm](), [bloom-filter](), [database](), [dynamodb](), [engineering](), [migration](), [recommendation-system](), [software-engineering]() ### AI overview Medium explains how it reduced the cost and maintenance burden of filtering for its Daily Digest by replacing Bloom Filters with user-based direct database queries. The article is part of a four-part series about incremental improvements to the recommendation algorithm. ### Source excerpt How we made our filtering 10x cheaper by removing our Bloom Filters Bloom Filters are great tools to make fast and cheap filtering. They also come with plenty of problems and can easily get expensive and cumbersome. We switched to user-based direct database queries, which made our filtering cheaper and easy to maintain. Here's the full breakdown of that migration. Intro: This is a 4-part series breaking down improvements to the algorithm behind the Medium's Daily Digest over the past year. When we started this work, the Digest was suboptimal -- and since it's a huge distribution surface, reaching millions of readers every day, we started working on incremental improvements.By the end of these projects, the digest was 10% more likely to convert users to paying members, less expensive to run, more flexible and easier to maintain and it's now providing higher quality recommendations for all our users, including our "power readers".This is told through the lens of our engineering team tackling a series of challenges one by one. Medium has a small team but we operate on a big scale. We're working our way through some technical debt and at the same time, striving to provide the best experience for our readers. This is the source of many interesting challenges.I hope this series helps you understand how the recommendations algorithm work and can help others who are facing similar technical challenges. This is probably the most technical story in the series, but I will keep it as simple as possible and hopefully this is interesting for non-technical readers too. Some Concepts Here's a little cheat sheet with some concepts you may need to follow along with this story You may need this to understand the rest of this postBloom Filters at Medium A lot of the filters I mention in this series are backed by Bloom Filters (I've described some of those filtering rules in Part 1 if you haven't read it already). We use Bloom filters to remove stories we think won't interest readers fro ## Built to Scale: How Nexxiot Delivers Always-On Global IoT with CockroachDB DevFeed: [Built to Scale: How Nexxiot Delivers Always-On Global IoT with CockroachDB]() Original publisher: [Read original article]() Author: David Weiss Published: 2025-05-30T00:00:00Z Content type: article Language: en Sources: [Cockroach Labs]() Topics: [CockroachDB](), [Databases](), [Internet of things](), [SQL](), [Availability](), [consistency](), [Resilience](), [DynamoDB](), [elasticsearch](), [Amazon RDS](), [upgrade](), [site-reliability-engineer]() Tags: [amazon-rds](), [availability](), [cloud](), [cockroachdb](), [consistency](), [database](), [databases](), [devices](), [distributed](), [dynamodb](), [elasticsearch](), [iot](), [resilience](), [site-reliability-engineer](), [sql](), [upgrade]() ### AI overview The article describes how Nexxiot selected CockroachDB for a global transport operations platform using solar-powered IoT devices. It explains that the company moved beyond DynamoDB and Elasticsearch as its workloads grew, citing concerns about transactional consistency, query complexity, scalability, availability, and operational overhead. ### Source excerpt When you're building a transport operations platform to optimize global fleets, "good enough" isn't good enough. ## Terraform module to manage Oxbow Lambda and its components DevFeed: [Terraform module to manage Oxbow Lambda and its components]() Original publisher: [Read original article]() Author: Oleh Motrunych Published: 2025-03-14T00:00:00Z Content type: release Language: en Sources: [Scribd Tech]() Topics: [Terraform](), [AWS Lambda](), [infrastructure as code (IAC)](), [Amazon Web Services](), [AWS Glue](), [Amazon Simple Queue Service (SQS)](), [data-processing](), [event driven](), [DynamoDB](), [Amazon S3]() Tags: [aws](), [aws-glue](), [aws-lambda](), [aws-s3](), [deltalake](), [dynamodb](), [event-driven](), [iac](), [oxbow](), [rust](), [security](), [sqs](), [terraform]() ### AI overview The article introduces terraform-oxbow, an open-source Terraform module for deploying and managing an Oxbow AWS Lambda workflow and its supporting components. It describes configurable integrations including AWS Glue, Kinesis Data Firehose, SQS, DynamoDB, IAM policies, and S3 notifications, while noting AWS notification limits and least-privilege considerations. ### Source excerpt Oxbow is a project to take an existing storage location which contains Apache Parquet files into a Delta Lake table. It is intended to run both as an AWS Lambda or as a command line application. We are excited to introduce terraform-oxbow, an open-source Terraform module that simplifies the deployment and management of AWS Lambda and its supporting components. Whether you're working with AWS Glue, Kinesis Data Firehose, SQS, or DynamoDB, this module provides a streamlined approach to infrastructure as code (IaC) in AWS. ## How We Run Terraform At Scale DevFeed: [How We Run Terraform At Scale]() Original publisher: [Read original article]() Author: Christian Monaghan Published: 2025-03-04T16:32:39Z Content type: article Language: en Sources: [Benchling]() Topics: [Terraform](), [Amazon Web Services](), [Amazon S3](), [DynamoDB]() Tags: [aws](), [cloud-infrastructure](), [dynamodb](), [infrastructure](), [infrastructure-as-code](), [management](), [operations](), [s3](), [software-engineering](), [terraform]() ### AI overview Benchling describes how it manages more than 165,000 cloud resources across 625 Terraform workspaces and 38 AWS accounts. The article explains that its earlier laptop-based Terraform workflow, using S3 state files and DynamoDB locks, became inefficient as the number of workspaces grew. ### Source excerpt Managing over 165k cloud resources across hundreds of workspaces could seem daunting. But for us, it's just another day at Benchling. Here's how we do it. We currently have: 165k cloud resources under management 625 Terraform workspaces 38 AWS accounts 170 engineers (40 of whom are infra specialists) We perform: 225 infrastructure releases daily (terraform apply operations) 723 plans daily (terraform plan operations) We've been successfully operating Benchling's infrastructure release system for the past two years (spoiler, it's Terraform Cloud), over which time we've doubled our infrastructure footprint with minimal additional release overhead. Before Terraform Cloud: The Chaos Our infra release process wasn't always this smooth. Let me rewind and take you back to how it was before. As is common guidance for small Terraform projects, our team would previously apply all infrastructure changes via laptop. Also in line with common guidance, our team used S3 to store state files, with DynamoDB state locks, which prevented any apply-time collisions. This is a great strategy for a small team working on up to a dozen workspaces. However, this slowly starts to break down as the team's workspace footprint grows. It's like the proverbial frog in the pot of water, slowly heated to a boil. By the time we made the switch, Benchling was managing 350 workspaces. We were approaching the boiling point. Pain Points: Developer Toil and Inefficiency Managing 350 workspaces with this approach had several downsides: Necessitated elevated AWS access permissions for the infrastructure team. It was time-consuming as the engineer had to navigate to each directory, run terraform apply, review and approve the run, then verify it succeeded. Very commonly a single change could affect over 120 workspaces, which would mean repeating this process 120 times. (We had developed a custom python script which helped parallelize this somewhat.) Accumulated infra drift. Often an engineer would go to apply ## The Making of a Scalable URL Shortener DevFeed: [The Making of a Scalable URL Shortener]() Original publisher: [Read original article]() Author: Cyril Mottier Published: 2025-02-18T10:57:54Z Content type: article Language: en Sources: [Cyril Mottier Blog]() Topics: [Code](), [Amazon Web Services](), [DynamoDB](), [Go Language](), [Requirements](), [Database]() Tags: [article](), [aws](), [build](), [distributed-system](), [dynamodb](), [engineering](), [go](), [redirection](), [requirements](), [retry](), [scale]() ### AI overview The article describes the design of a custom URL shortener built with AWS Lambda, AWS DynamoDB, and Go. It outlines requirements such as fast redirection, short URLs, batch creation, and optional expiration, then examines slug-generation strategies including randomized slugs, incremental counters, and permutation of the slug space. ### Source excerpt This article was initially published as a thread of posts on X. For the sake of readability and long-term availability, it is now available here as a single-page article. Post 1 A few months ago, I built a custom URL shortener. Quick refresher: a URL shortener does two things: Creates short URLs that map to long URLs Redirects short URL requests to those long URLs. Designing this was a fantastic engineering challenge. Here's a look at my design process! ## DynamoDB For System Design Interviews DevFeed: [DynamoDB For System Design Interviews]() Original publisher: [Read original article]() Author: Evan King Published: 2024-11-19T21:30:02Z Content type: tutorial Language: en Sources: [Evan King]() Topics: [DynamoDB](), [Amazon Web Services](), [NoSQL](), [Database](), [Transactions](), [Provisioning](), [Application Development](), [configuration](), [Learning](), [vendor lock-in]() Tags: [aws](), [configuration](), [data-storage](), [database](), [dynamodb](), [learning](), [nosql](), [provisioning](), [system-design-interview](), [transactions]() ### AI overview This tutorial explains DynamoDB for system design interviews. It covers DynamoDB's managed operation, scalability, key-value and NoSQL data model, transaction support, lack of open-source internals, and practical considerations including vendor lock-in and whether interviewers permit its use. ### Source excerpt By the Co-founder of www.hellointerview.comIntro DynamoDB is a fully-managed, highly scalable, key-value service provided by AWS. Cool, buzz-words. But what the hell does that mean and why does it matter? Fully-Managed -- This means that AWS takes care of all the operational aspects of the database. The fully-managed nature allows AWS to handle all operational tasks -- hardware provisioning, configuration, patching, and scaling -- freeing developers to concentrate on application development. Highly Scalable -- DynamoDB can handle massive amounts of data and traffic. It automatically scales up or down to adjust to your application's needs, without any downtime or performance degradation. Key-value -- DynamoDB is a NoSQL database, which means it doesn't use the traditional relational database model. Instead, it uses a key-value model that allows for flexible data storage and retrieval. The moral of the story is that DynamoDB is a super easy to use and can scale to support a wide variety of applications. For system design interviews in particular, it has just about everything you'd ever need from a database. It even supports transactions now! Which neutralizes one of the biggest criticisms of DynamoDB in the past. Importantly, DynamoDB is not open-source, so we can't as easily describe its internals like we did with breakdowns of open source technologies like Kafka and Redis. Instead, we'll focus more on you interact with it. In order to look under the hood, we'll rely on the limited information AWS provides via documentation and the DynamoDB Whitepaper. In this deep dive, we'll break down exactly what you need to know about DynamoDB in order to field any question about it in a system design interview. Along the way, you'll also acquire practical learning that you can later apply in your own projects. Let's break it down! Candidates often ask me, "am I even allowed to use DynamoDB in an interview?"The answer is simple, ask your interviewer! Many will say yes, and just expec ## Application Architecture: Combining DynamoDB and Tinybird DevFeed: [Application Architecture: Combining DynamoDB and Tinybird]() Original publisher: [Read original article]() Author: Alasdair Brown Published: 2024-11-06T00:00:00Z Content type: tutorial Language: en Sources: [Tinybird]() Topics: [App](), [Transactions]() Tags: [architecture](), [dynamodb](), [scalable-analytics-architecture]() ### AI overview This article explains how to combine DynamoDB for transactions with Tinybird for analytics, addressing their differing performance characteristics. ### Source excerpt DynamoDB is fast for transactions but slow for analytics. Here's how to combine it with Tinybird and get the best of both worlds. ## Query DynamoDB tables with SQL DevFeed: [Query DynamoDB tables with SQL]() Original publisher: [Read original article]() Author: Cameron Archer Published: 2024-10-30T00:00:00Z Content type: tutorial Language: en Sources: [Tinybird]() Topics: [DynamoDB](), [SQL](), [NoSQL]() Tags: [dynamodb](), [nosql](), [sql](), [the-data-base]() ### AI overview The article presents querying DynamoDB tables with SQL and says Tinybird makes NoSQL data accessible to people who know SQL, avoiding PartiQL. ### Source excerpt Query DynamoDB with SQL instead of fighting with PartiQL. Tinybird makes your NoSQL data accessible to anyone who knows SQL. ## The Tinybird DynamoDB Connector is now GA DevFeed: [The Tinybird DynamoDB Connector is now GA]() Original publisher: [Read original article]() Author: Cameron Archer Published: 2024-10-28T00:00:00Z Content type: release Language: en Sources: [Tinybird]() Topics: [DynamoDB]() Tags: [dynamodb](), [product-updates](), [production]() ### AI overview Tinybird announces that its DynamoDB Connector is production-ready, claiming sub-second queries across partitions and unlimited indexing. ### Source excerpt The DynamoDB Connector is now production-ready. Sub-second queries across partitions, unlimited indexing. No more workarounds. ## Simple patterns for aggregating on DynamoDB DevFeed: [Simple patterns for aggregating on DynamoDB]() Original publisher: [Read original article]() Author: Cameron Archer Published: 2024-10-15T00:00:00Z Content type: tutorial Language: en Sources: [Tinybird]() Topics: [DynamoDB](), [data]() Tags: [dynamodb](), [patterns](), [scalable-analytics-architecture]() ### AI overview The article presents four approaches for aggregating data in DynamoDB tables, addressing the fact that DynamoDB does not natively support aggregations. ### Source excerpt DynamoDB doesn't natively support aggregations, so here are four different approaches to aggregate data in DynamoDB tables. ## Introducing the Tinybird DynamoDB Connector in public beta DevFeed: [Introducing the Tinybird DynamoDB Connector in public beta]() Original publisher: [Read original article]() Author: Cameron Archer Published: 2024-09-20T00:00:00Z Content type: release Language: en Sources: [Tinybird]() Topics: [DynamoDB]() Tags: [dynamodb](), [partition](), [product-updates]() ### AI overview Tinybird's DynamoDB Connector has entered public beta, enabling cross-partition queries in milliseconds. ### Source excerpt The DynamoDB Connector just hit public beta. Cross-partition queries in milliseconds are now possible. Test it yourself. ## 5 Best Use Cases for DynamoDB in 2026 | Tinybird DevFeed: [5 Best Use Cases for DynamoDB in 2026 | Tinybird]() Original publisher: [Read original article]() Author: Cameron Archer Published: 2024-09-18T00:00:00Z Content type: article Language: en Sources: [Tinybird]() Topics: [DynamoDB]() Tags: [2026](), [dynamodb](), [the-data-base](), [use-cases]() ### AI overview The article examines five use cases for DynamoDB in 2026, including situations where it performs well and where it may struggle. It emphasizes choosing the right tool before migration becomes necessary. ### Source excerpt DynamoDB use cases where it excels and where it struggles. Pick the right tool before you're stuck migrating later. ## How coupling distinguishes monoliths, microservices, and serverless architectures DevFeed: [How coupling distinguishes monoliths, microservices, and serverless architectures]() Original publisher: [Read original article]() Author: Mike Published: 2024-01-19T05:42:25Z Content type: opinion Language: en Sources: [Dev Details]() Topics: [Architecture & Design](), [Microservice](), [Microservices](), [Serverless](), [Databases](), [DynamoDB]() Tags: [coupling](), [database](), [distributed](), [dynamodb](), [microservices](), [monolith](), [serverless]() ### AI overview This article examines how to distinguish monoliths, microservices, and serverless architectures. It argues that deployment coupling and dependencies matter more than the number of repositories, containers, lambdas, teams, or database servers. Applications that must be redeployed together are described as parts of a distributed monolith, while microservices are characterized by how tightly coupled they are to dependencies, one another, and data. ### Source excerpt Or are they? ## 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... [Next page]()