# sns Published articles for sns. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Five Years of Kafka at Razorpay's UPI Switch DevFeed: [Five Years of Kafka at Razorpay's UPI Switch]() Original publisher: [Read original article]() Author: Kshitij Nawandar Published: 2026-09-07T09:09:58Z Content type: article Language: en Sources: [Razorpay Engineering - Medium]() Topics: [Kafka](), [Architecture & Design](), [event driven](), [Amazon Simple Queue Service (SQS)](), [distributed-systems](), [Microservices](), [Amazon Web Services]() Tags: [aws](), [aws-sns](), [distributed-systems](), [event-driven](), [kafka](), [microservices](), [razorpay](), [redshift](), [sns](), [sqs]() ### AI overview Razorpay describes five years of evolution in its UPI Switch, from a monolith using AWS SQS to Kafka-based infrastructure. The article covers architectural decisions, operational challenges, and optimization work affecting payment-processing performance, reliability, and scale. ### Source excerpt Preface The UPI Switch at Razorpay has evolved significantly in the five years since we started building it. The Switch is the platform that enables real-time payment processing with NPCI. When the team began, it was little more than an idea. Today it powers more than 70% of Razorpay's total UPI volume. Because UPI is inherently asynchronous, a messaging system sits at the heart of the Switch and has a direct impact on performance, reliability, and scale. What began as a straightforward queue became the core of the system, shaping how every new feature was designed and delivered. This post covers that evolution: the decisions that enabled growth, the ones that slowed us down, the operational issues that forced us to rethink our assumptions, and the optimizations that ultimately stabilized our Kafka-based infrastructure. This is the story of what we got right, what we got wrong, and how we eventually built something stable enough to grow on. The First Version: Monolith and SQS When we began building the UPI Switch, we weren't thinking about massive scale, distributed systems, or elegant event routing. So we built Switch v1 as a monolith. No microservices, no distributed orchestration: just one solid block of code doing everything. That was the right call. We needed to move fast, experiment, and learn, and we followed the Keep It Simple, Stupid (KISS) principle deliberately. For messaging, we picked AWS SQS: reliable, managed, and low on cognitive load. We didn't need ordering guarantees at the time, so a standard queue worked fine. We started with just two queues, and this setup held its ground. It handled a peak of 400 TPS during the IPL. The limitations showed up as the ecosystem grew. A single event, like a successful payment, needed to fan out into multiple workflows: Update NPCI with an API call Send callbacks to merchants about payment status Push structured data into our warehouse (AWS Redshift) To handle this, we started bolting on AWS SNS plus SQS for fan-ou ## Building Reliable Messaging Patterns in AWS with SQS and SNS DevFeed: [Building Reliable Messaging Patterns in AWS with SQS and SNS]() Original publisher: [Read original article]() Author: Guillermo Ojeda Published: 2024-12-20T15:20:38Z Content type: tutorial Language: en Sources: [Guille Ojeda]() Topics: [Amazon Simple Queue Service (SQS)](), [Amazon Web Services](), [Messaging](), [distributed-systems]() Tags: [architecture](), [aws](), [distributed-system](), [distributed-systems](), [error-handling](), [fifo](), [messaging](), [scale](), [sns](), [sqs](), [technical]() ### AI overview This tutorial explains how to build reliable distributed messaging systems with AWS SQS and SNS. It covers queue configuration, error handling, scaling patterns, message retention, long polling, visibility timeouts, and the differences between Standard and FIFO queues, using Node.js examples. ### Source excerpt Building distributed systems requires putting a lot of attention on communication between components. These components often need to exchange information asynchronously, and that's where message queues and pub/sub systems are the go-to solution. AWS ... ## Micro Service Events (AWS EventBridge vs. SNS) DevFeed: [Micro Service Events (AWS EventBridge vs. SNS)]() Original publisher: [Read original article]() Author: Kristian Müller Published: 2022-09-16T12:06:51Z Content type: comparison Language: en Sources: [Homegate Engineering Blog - Medium]() Topics: [event driven](), [Messaging](), [Amazon Web Services](), [Amazon Simple Queue Service (SQS)](), [Publish-subscribe pattern](), [Architecture & Design](), [AWS Lambda](), [Amazon API Gateway](), [DynamoDB](), [Amazon S3](), [TypeScript]() Tags: [api](), [api-gateway](), [architecture](), [aws](), [aws-eventbridge](), [aws-sns](), [data](), [data-processing](), [database](), [dynamodb](), [event](), [event-driven](), [eventbridge](), [eventbus](), [events](), [gateway](), [lambda](), [messaging](), [microservice-architecture](), [s3](), [sns](), [sqs](), [typescript]() ### AI overview This article examines Homegate's event-driven microservice architecture on AWS and compares Amazon SNS with EventBridge as messaging infrastructure. It describes how services use SNS, SQS, Lambda, API Gateway, DynamoDB, and S3, and explains that EventBridge has a different focus, so replacing SNS is not necessarily the right question. ### Source excerpt During the last few years, we've been busy rebuilding the Homegate platform in an event driven, micro service architecture based on AWS. One of the most important buildings blocks, or shall we rather say the mortar that connects the building blocks, is the way our services send events to each other. While Amazon offers its notification service SNS since more than 12 years; in 2019 it started a newcomer in the messaging bus field by introducing EventBridge. This was very interesting to us as we're using SNS a lot. Bus on a Bridge (Photo by "Blue Elf" on Wikimedia Commons) As EventBridge was introduced and gained traction we asked ourselves what advantaged we would gain by switching our SNS based service glue to EventBridge. Maybe this turned out to be the wrong question for us. Let's look at our system to see why. Micro Services Connected by SNS Homegate is connecting dozens of services via SNS right now. Those services range from single purpose Lambda functions written in TypeScript to database heavy services using Aurora or DynamoDB and Step Functions sending events to other services. Some of the services are also interfacing external APIs such as Salesforce or SendGrid. Here our Lambda functions can call APIs when needed. On the other hand, we have web services to be called as APIs by other systems and end users. This is mostly achieved via API Gateway. A typical service connects to the system by subscribing to events needed to fulfill its purpose. Those inbound SNS events are then ingested via SQS allowing for orderly data processing and DLQs to handle errors. Outgoing results are pushed back to the system in form of events to a SNS topics. As Marko Savic has pointed out, we're also utilizing S3 to store and replay those events if needed. Basic AWS Lambda function connected via SNS If we would replace the SNS topic by streams on the EventBridge we would initially gain the same functionality by utilizing SQS as well. However, EventBridge has a different focus. Let ## Use AWS SNS to send data to Tinybird DevFeed: [Use AWS SNS to send data to Tinybird]() Original publisher: [Read original article]() Author: David Manzanares Published: 2022-07-12T00:00:00Z Content type: tutorial Language: en Sources: [Tinybird]() Topics: [Messaging](), [Amazon Web Services](), [data]() Tags: [aws](), [aws-sns](), [data](), [product-updates](), [sns]() ### AI overview A tutorial on using AWS SNS, a pub/sub messaging system, to send data to Tinybird. ### Source excerpt SNS is a popular pub/sub messaging system for AWS users. Here's how to use SNS to send data to Tinybird. ## Replaying events using AWS S3 DevFeed: [Replaying events using AWS S3]() Original publisher: [Read original article]() Author: Marko Savic Published: 2022-04-08T08:40:24Z Content type: tutorial Language: en Sources: [Homegate Engineering Blog - Medium]() Topics: [Amazon S3](), [event driven](), [Microservices](), [Amazon Web Services](), [AWS CloudFormation](), [Serverless]() Tags: [aws](), [aws-lambda](), [aws-s3](), [cloudformation](), [debugging](), [event-driven](), [event-sourcing](), [lambda](), [microservices](), [s3](), [serverless](), [sns](), [software-architecture](), [versioning]() ### AI overview This tutorial describes replaying failed events in serverless, event-driven microservices by storing AWS Step Functions events in versioned Amazon S3 objects. It also outlines using a dead-letter queue and Lambda to retrieve the latest event and trigger the workflow again. ### Source excerpt In the world of event-driven microservices one of the important tasks is to have a mechanism to recover from failed events, as well as to have possibility to replay certain events. We at Homegate deal with serverless microservices. If, at any of these services, we encounter a failure during processing of an resource, we might need to recreate the last event or series of events for given resource. Usecase The central part of ingestion process is AWS StepFunctions that lives in a, so called, importer service. It is responsible for various enrichments and conditional actions on resource. The last step of the step function is sending an event to the AWS SNS which is consumed by many downstream services as you can see in the following diagram. Step functions as central part of ingestion process Here, many open questions can be identified: How to deal with failure within AWS StepFunctions? How to recreate SNS event if needed for downstream system? Would it be possible to replay the whole step function? Solution Possible answer to these questions would be to store events that goes through AWS StepFunctions into some kind of event storage, in Homegate case, we chose AWS S3. In order to support the whole history of events for given resource, S3 bucket versioning is used. This is quite handy for replaying series of events or even for debugging purposes. Here is a sample CloudFormation which enables that functionality: EventLogBucket: Type: 'AWS::S3::Bucket' Properties: BucketName: 'sample-unnique-bucket-name' AccessControl: Private PublicAccessBlockConfiguration: BlockPublicAcls: true BlockPublicPolicy: true IgnorePublicAcls: true RestrictPublicBuckets: true Tags: - Key: service Value: sample-service - Key: env Value: !FindInMap [ Stages, !Ref Stage , Env ] VersioningConfiguration: Status: Enabled That way we can replay the whole step functions flow using last logged event for given resource. That functionality could be available from different places, like DLQ or via api end ## AWS FIFO Queues with Message Groups for Atomic Processing at Scale DevFeed: [AWS FIFO Queues with Message Groups for Atomic Processing at Scale]() Original publisher: [Read original article]() Author: Nick De Cooman Published: 2022-03-03T09:55:58Z Content type: tutorial Language: en Sources: [Homegate Engineering Blog - Medium]() Topics: [Amazon Simple Queue Service (SQS)](), [Amazon Web Services](), [Serverless](), [event driven](), [Scalability](), [Publish-subscribe pattern]() Tags: [architecture](), [aws](), [decoupling](), [distributed](), [event](), [event-driven](), [events](), [fifo](), [serverless](), [sns](), [sqs]() ### AI overview A tutorial on using AWS FIFO queues and message groups to process events for multiple objects in parallel while ensuring that only one instance processes events for the same object at a time. It presents a serverless architecture using SNS, SQS, and Lambda for atomic listing ingestion. ### Source excerpt How to process multiple objects in parallel with the guarantee that for each unique object, only a single instance is processed simultaneously In a distributed, event-driven architecture, many challenges arise when processing large sets of data. One of them is the need for scalability when handling event streams with a substantial throughput. The challenge becomes even more complicated when processing these events involves atomic operations. In this case, we cannot simply process multiple events in parallel. As an example, let's say that we are building a service that ingests listings into our platform. The ingestion process involves several steps and requires multiple API operations. As a result, the whole ingestion can take up to several seconds per listing to complete. Now, here is where it gets tricky: a constraint of our service is that, while we are processing a listing, we cannot process other events for the same listing. The ingestion should be considered an atomic operation, and hence, only a single execution per listing can be performed simultaneously. At Homegate, we run most of our infrastructure on AWS in a serverless fashion. At the core of this, Lambda functions execute application logic. A common pattern is to use SNS topics to fan-out messages, and SQS for acting as a decoupling buffer between an SNS topic and some Lambda function. For our ingester service, a logical architecture could look like this: Listing events are published to a dedicated SNS topic. An SQS queue subscribes to this topic and consumes the events. Here, the events are buffered until a Lambda instance removes them from the queue and processes the corresponding listing. Under normal circumstances -- in which the processing would not involve an atomic operation -- this architecture would be a good fit. It would scale automatically relative to the number of Lambda instances that consume events from the queue, without much additional configuration. However, in the case where we cannot p ## Should we consider migrate to Amazon EventBridge from Amazon SNS + SQS ? DevFeed: [Should we consider migrate to Amazon EventBridge from Amazon SNS + SQS ?]() Original publisher: [Read original article]() Author: Guillaume Lannebere Published: 2022-02-01T14:45:47Z Content type: article Language: en Sources: [betclic-tech - Medium]() Topics: [Amazon EventBridge](), [Amazon Simple Queue Service (SQS)](), [event driven](), [Architecture & Design](), [Microservice](), [Amazon Web Services](), [Monitoring](), [Latency]() Tags: [amazon-eventbridge](), [architecture](), [aws](), [event-driven](), [eventbridge](), [latency](), [microservices](), [monitoring](), [net](), [sns](), [sqs]() ### AI overview The article examines Betclic's event-driven architecture requirements and its early-2019 decision to combine Amazon SNS with Amazon SQS. It presents the decision in the context of unpredictable traffic, multiple consumers, event replay, monitoring, schema management, language interoperability, analytics, autoscaling, low latency, high availability, AWS integration, and cost. ### Source excerpt Should we consider migrating to Amazon EventBridge from Amazon SNS + SQS? In early 2019, Betclic was in a corner, and we decided to migrate to microservices approach and migrate to the cloud. The first step of this change was the implementation of an Event Driven Architecture (EDA). Betclic presentation Before speaking of architecture and EDA, it's essential to know what Betclic is doing to understand our choice quickly. Betclic is a French online gambling company. Betclic operates in Sports betting, horse race betting, and online poker. The specificity of gambling is that we don't have predictive traffic. We have high traffic peaks during significant events and weekends. We can see an example of traffic on a soccer event: Example of trafficEDA wishlist When beginning 2019, we did a workshop to identify the key features of our ideal EDA; we would like something: allowing multiple reads: the same message can be read by various services with error recovery: in case of error, we would like to replay events easy to monitor: particularly with Datadog, our centralized monitoring solution with schema registry language-agnostic: we use 3 main languages .net, Kotlin, and Typescript (for Lambda), and the chosen solution must work with all languages with analytics capabilities: we would like to integrate all our events of EDA in our data lake fully managed with auto scaling: essential feature for us; we have unpredictable traffic with big spikes at the end of matches, so we need to have an EDA to be able to absorb our spikes with low latency and high availability: also an essential feature, we would like our EDA to serve critical use cases for our final users, so we want something the most real-time and the most available possible well-integrated with AWS services: EDA is one of our first bricks in the cloud, but we want something to help us to migrate to AWS easy to use: we want all the time something easy to use :) but here it's particularly true because EDA is a centralized