# event driven An event-driven architecture is a software architecture in which events trigger communication between decoupled services. 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 ## Human judgment doesn't leave the software factory. It relocates. DevFeed: [Human judgment doesn't leave the software factory. It relocates.]() Original publisher: [Read original article]() Author: Addy Osmani Published: 2026-08-21T14:31:12Z Content type: article Language: en Sources: [Elevate]() Topics: [Software](), [Testing](), [event driven](), [Architecture & Design](), [GitHub](), [pull-requests](), [mutation-testing](), [Security]() Tags: [agent](), [architecture](), [claude-code](), [codex](), [event-driven](), [github](), [mutation-testing](), [pull-requests](), [quality](), [security](), [software](), [testing]() ### AI overview This field guide explains how to build a repeatable software factory while preserving human judgment and ownership. It recommends human involvement in product intent, system design, quality standards, code review, and merge decisions, supported by automated checks and deliberate experimentation. The article argues that a factory is most useful when work must be repeatable and event-driven across queues such as GitHub issues, Slack, Linear, or a backlog. ### Source excerpt A field guide to building a software factory that still has an owner. ## Human judgment doesn't leave the software factory. It relocates. DevFeed: [Human judgment doesn't leave the software factory. It relocates.]() Original publisher: [Read original article]() Author: Addy Osmani Published: 2026-08-21T00:00:00Z Content type: article Language: en Sources: [Addy Osmani]() Topics: [Software](), [coding](), [event driven](), [implementation](), [GitHub](), [Claude Code](), [codex]() Tags: [agent](), [architecture](), [automated](), [batch](), [build](), [claude-code](), [codex](), [event-driven](), [github](), [linear](), [maintainability](), [mutation-testing](), [pull-requests](), [quality](), [scanners](), [slack](), [software](), [testing](), [verification]() ### AI overview A field guide to building a repeatable software factory while keeping humans responsible for product intent, system design, quality standards, code review, and final merge decisions. It recommends early and continuous quality checks, deliberate constraints, and event-driven automation when ordinary coding workflows are no longer sufficient. ### Source excerpt A field guide to building a software factory that still has an owner. ## New in Confluent Cloud and WarpStream: Evolving the Data Streaming Platform for AI, Scale, and Control DevFeed: [New in Confluent Cloud and WarpStream: Evolving the Data Streaming Platform for AI, Scale, and Control]() Original publisher: [Read original article]() Author: Mike Agnich Published: 2026-08-18T14:00:11Z Content type: article Language: en Sources: [Confluent: Data in motion]() Topics: [Streaming](), [Kafka](), [Cloud](), [event driven](), [Transactions]() Tags: [confluent](), [confluent-cloud](), [debug](), [kafka](), [networking](), [security](), [stream-processing](), [streaming](), [transactions]() ### AI overview Confluent describes more than 70 new features for its data streaming platform, including new connectors, networking improvements, AI capabilities for event-driven agents, real-time machine learning in streaming pipelines, and AI-assisted development tools. The article also discusses broader Kafka workload support, including stream processing, transactions, exactly-once processing, queues, debugging, and Python client support for Queues for Kafka. ### Source excerpt Accelerate enterprise streaming and AI workloads with new product updates across Kora, connectors, Flink, Tableflow, AI anomaly detection and forecasting, security enhancements, and Warpstream. ## Fresh context: change data capture, not batch ETL DevFeed: [Fresh context: change data capture, not batch ETL]() Original publisher: [Read original article]() Author: Simran Regmi Published: 2026-08-12T00:00:00Z Content type: tutorial Language: en Sources: [Redis Blog]() Topics: [event driven](), [data](), [Database](), [PostgreSQL]() Tags: [agent](), [applications](), [batch](), [canada](), [customer](), [data](), [database](), [event](), [event-driven](), [events](), [guide](), [postgresql](), [systems](), [tech-de]() ### AI overview This guide explains how change data capture (CDC) keeps agent context current by forwarding database inserts, updates, and deletes as events. It contrasts CDC with nightly batch ETL, which can leave agents using stale policies or pricing, and describes log-based, trigger-based, and polling approaches. ### Source excerpt In many systems, the reason an agent quotes yesterday's data isn't the model. It's the pipeline behind it: a nightly ETL job that refreshed the agent's context hours ago. Change data capture (CDC) can shrink that staleness window from hours to seconds... ## We turned off Pub/Sub and nobody noticed DevFeed: [We turned off Pub/Sub and nobody noticed]() Original publisher: [Read original article]() Author: Patrick Hamann; Mike Fisher Published: 2026-08-11T13:56:40Z Content type: article Language: en Sources: [The incident.io Blog]() Topics: [event driven](), [Messaging](), [Publish-subscribe pattern](), [Availability](), [systems](), [Apache Pulsar](), [Cloud](), [Google]() Tags: [availability](), [broker](), [cloud](), [event-driven](), [incident](), [incident-channel](), [incident-management](), [incident-response](), [messaging](), [outage](), [post-mortem](), [production](), [scale](), [slack](), [slack-incident]() ### AI overview This developer article explains how incident.io made its predominantly event-driven platform more resilient by adding a secondary message broker alongside Google Cloud Pub/Sub. It describes the role of message brokers and publish-subscribe processing, the risks of a single point of failure, and the successful production test in which Pub/Sub was turned off without affecting customers. ### Source excerpt Our entire event-driven platform ran through a single message broker, which made it a single point of failure. So we added a second one. This is the story of building an event load balancer, the queuing theory behind it, and the final chaos test where we turned off Pub/Sub in production and nobody noticed. ## Worth Reading 080126 DevFeed: [Worth Reading 080126]() Original publisher: [Read original article]() Author: Russ Published: 2026-08-01T13:09:17Z Content type: article Language: en Sources: [rule 11 reader]() Topics: [DevOps](), [End-to-End Encryption](), [BGP](), [Encryption](), [Internet](), [Latency](), [Cloud](), [cloud-infrastructure](), [Databases](), [event driven](), [Artificial Intelligence]() Tags: [ai](), [article](), [bgp](), [cloud](), [databases](), [devops](), [encryption](), [event-driven](), [internet](), [latency](), [software](), [storage](), [systems](), [worth-reading]() ### AI overview A curated "Worth Reading" roundup covering goodput and latency targets, the efficiency costs of Big AI, internet infrastructure and BGP's ORIGIN attribute, and current debates over end-to-end encryption. ### Source excerpt The honest target is goodput, meaning requests per second that actually met your latency targets. Count anything else and you are counting cold plates. I find it strange that as a software industry, we put in so much effort to make our systems efficient to design, build, and scale with DevOps practices, cloud native architectures, event-driven functions, optimized databases and storage, and resource-sipping Lean IT -- then we threw it all out the window when Big AI came along. If there is one universal law in internet infrastructure, it is that no good intention goes unpunished by the practical realities of legacy software maintenance. This Article updates and expands on 2012 research on encryption and globalization, analyzing what the authors call "Round 3" of the Going Dark Debate: the current controversies over end-to-end encryption (E2EE). Using our unique position on the Internet, we took an investigative look at one of the well-known mandatory attributes in BGP, the ORIGIN attribute. ## Product release notes - May 2026 DevFeed: [Product release notes - May 2026]() Original publisher: [Read original article]() Author: Etay Alony Published: 2026-07-30T10:06:41Z Content type: release Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [Release notes](), [event driven](), [Claude Code](), [configuration](), [Code]() Tags: [2026](), [agentic](), [claude-code](), [code](), [community](), [configuration](), [copilot](), [event-driven](), [model](), [plugin](), [product](), [product-release](), [release](), [ui](), [workflows]() ### AI overview Port's May 2026 release adds a public plugins repository, multiple workflow triggers and placement controls, Claude Code and Copilot integrations, organization-aware browser sessions, and an opt-in sidebar layout. ### Source excerpt May extends what teams can build on Port: a public plugins repo opens the ecosystem to community use and contribution, Workflows gain multiple triggers and richer placement controls, and two new integrations bring Claude Code and Copilot activity directly into Port. ## How to Connect Prometheus Alerts to an Event-Driven AI Agent for Initial Investigation DevFeed: [How to Connect Prometheus Alerts to an Event-Driven AI Agent for Initial Investigation]() Original publisher: [Read original article]() Author: Pramodh Kumar M Published: 2026-07-23T15:00:31Z Content type: tutorial Language: en Sources: [Kubernetes - KodeKloud Blog | DevOps, Cloud, Kubernetes, AI Tutorials & More]() Topics: [AI Agent](), [Prometheus](), [event driven](), [Kubernetes](), [Slack](), [Deployment]() Tags: [agents](), [ai](), [ai-agents](), [aiops](), [alert-fatigue](), [alert-manager](), [alert-triage](), [architecture](), [auto-remediation](), [automated-incident-response](), [automation](), [devaiops](), [event-driven](), [event-driven-ai-agents-with-prometheus-alerts](), [guide](), [how-to](), [kubernetes](), [kubernetes-ai-agent](), [prometheus](), [prometheus-alert-rules](), [prometheus-alertmanager-webhook](), [root-cause-analysis](), [slack](), [sre](), [sre-automation]() ### AI overview This guide explains how to connect Prometheus and Alertmanager to an event-driven AI agent that investigates alerts before a human responds. It covers the architecture, read-only investigation tools, alert-rule annotations, safety guardrails, and a progression toward guarded remediation. ### Source excerpt Every page interrupts a human, yet most alerts end in the same ten investigation steps. Here is how event driven AI agents catch Prometheus alerts and do that first pass before you even look at your phone. ## Durable Objects are Made for Agents DevFeed: [Durable Objects are Made for Agents]() Original publisher: [Read original article]() Author: Calvin French-Owen Published: 2026-07-22T12:00:00Z Content type: opinion Language: en Sources: [Calvin French-Owen]() Topics: [Cloudflare](), [Serverless](), [SQLite](), [V8](), [event driven](), [WebSocket](), [wasm]() Tags: [cloudflare](), [event-driven](), [serverless](), [sqlite](), [v8](), [websockets]() ### AI overview An opinionated technical article explains why Cloudflare Durable Objects can be a strong primitive for building agents and other stateful products. It describes their serverless V8 execution model, per-object SQLite storage, ID-based request routing, event-driven structure, and potential shortcomings. ### Source excerpt I've spent the last few months building almost exclusively on Cloudflare Durable Objects. They're a near-perfect primitive for building agents--here's where they shine, and where they fall short. ## Announcing Upvest as Confluent's 2026 EMEA Data Streaming Startup of the Year DevFeed: [Announcing Upvest as Confluent's 2026 EMEA Data Streaming Startup of the Year]() Original publisher: [Read original article]() Author: Tim Graczewski Published: 2026-07-16T00:07:01Z Content type: article Language: en Sources: [Confluent: Data in motion]() Topics: [Streaming](), [real-time](), [Resilience](), [event driven](), [Scalability](), [Cloud](), [data-governance](), [Microservices](), [Architecture & Design](), [Disaster Recovery]() Tags: [2026](), [cloud](), [cloud-native](), [compliance](), [confluent](), [confluent-cloud](), [data](), [data-governance](), [disaster-recovery](), [emea](), [event-driven](), [fintech](), [infrastructure](), [microservices](), [real-time](), [resilience](), [revolut](), [scalability](), [startup](), [streaming]() ### AI overview Confluent recognizes Berlin-based Upvest as its inaugural EMEA Data Streaming Startup of the Year. The article describes Upvest's real-time, event-driven fintech infrastructure, which supports embedded investments, client onboarding, data governance, disaster recovery, scalability, and regulatory resilience for major financial institutions. ### Source excerpt Confluent for Startups provides an easy on-ramp to Confluent Cloud for early stage startups with great data streaming use cases. ## 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. ## Most System Design Mistakes Hide Between the Boxes DevFeed: [Most System Design Mistakes Hide Between the Boxes]() Original publisher: [Read original article]() Author: Raul Junco Published: 2026-05-30T12:16:25Z Content type: article Language: en Sources: [System Design Classroom]() Topics: [event driven](), [Databases](), [API](), [cloud-infrastructure](), [dashboards]() Tags: [api](), [architecture](), [cloud-infrastructure](), [dashboards](), [databases](), [event-driven](), [logs]() ### AI overview The article examines system design failures that occur between services and components, such as events not being published after database commits, replica lag, overloaded queues, and schema changes affecting consumers. It emphasizes that interactions among databases, queues, caches, APIs, logs, dashboards, and cloud infrastructure create failure patterns that architecture diagrams may not reveal. ### Source excerpt Five gaps your architecture diagrams don't show ## Your event-driven architecture is a distributed monolith -- and that's fine, if you measure it DevFeed: [Your event-driven architecture is a distributed monolith -- and that's fine, if you measure it]() Original publisher: [Read original article]() Author: fidelitas.io Published: 2026-05-15T10:30:00Z Content type: opinion Language: en Sources: [fidelitas.io]() Topics: [event driven](), [Messaging](), [Structured-data](), [Traces](), [interface]() Tags: [architecture](), [coupling](), [decoupling](), [distributed-systems](), [event-driven-architecture](), [messaging](), [observability](), [schemas](), [services](), [traces]() ### AI overview This opinion article argues that event-driven systems can become distributed monoliths when message brokers hide rather than remove coupling. It focuses on explicit, versioned, validated schemas and traces as ways to reveal dependencies and diagnose failures. ### Source excerpt Async messaging didn't decouple your services; it hid the coupling. Here's how to find the seams again with traces, schemas, and a healthy fear of fan-out. ## Event-Driven Systems Are Easy to Build but Hard to Keep Correct DevFeed: [Event-Driven Systems Are Easy to Build but Hard to Keep Correct]() Original publisher: [Read original article]() Author: Raul Junco Published: 2026-04-25T11:52:06Z Content type: article Language: en Sources: [System Design Classroom]() Topics: [event driven](), [distributed-systems](), [Software Engineering]() Tags: [bugs](), [distributed-systems](), [event-driven](), [schema](), [systems]() ### AI overview This article explains that event-driven systems can appear operationally healthy while producing incorrect business outcomes. It focuses on correctness failures caused by duplicate delivery, ordering gaps, dual writes, retries, and schema drift. ### Source excerpt 4 ways event-driven systems fail without looking broken ## An Interactive Study of Publish/Subscribe Messaging for Event-Driven Backend Systems DevFeed: [An Interactive Study of Publish/Subscribe Messaging for Event-Driven Backend Systems]() Original publisher: [Read original article]() Author: Ivan Cernja Published: 2026-04-24T00:00:00Z Content type: tutorial Language: en Sources: [Encore Updates]() Topics: [Publish-subscribe pattern](), [Messaging](), [Back end](), [distributed-systems](), [event driven]() Tags: [decoupling](), [developers](), [distributed-systems](), [event-driven](), [events](), [flow](), [messaging](), [patterns](), [publisher](), [subscriber]() ### AI overview An interactive tutorial explains publish/subscribe messaging by contrasting it with direct service calls. It shows how topics decouple publishers from subscribers, support fan-out, and let subscribers process messages independently, including when downstream services are unavailable. ### Source excerpt An interactive study of publish/subscribe messaging ## Workspace agents DevFeed: [Workspace agents]() Original publisher: [Read original article]() Published: 2026-04-22T10:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [ChatGPT](), [Artificial Intelligence](), [event driven](), [API]() Tags: [agents](), [ai](), [api](), [chatgpt](), [how-to](), [openai-academy](), [workflows]() ### AI overview An introduction to workspace agents in ChatGPT, explaining how they support repeatable workflows through triggers, processes, specialized skills, tools, and connected systems. ### Source excerpt Learn how to build and use workspace agents in ChatGPT to automate repeatable workflows, connect tools, and streamline team operations. ## Multi-Tenant Billing Architecture: Design Patterns for SaaS DevFeed: [Multi-Tenant Billing Architecture: Design Patterns for SaaS]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-04-15T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [Architecture & Design](), [Multi-tenancy](), [Software as a service](), [event driven]() Tags: [architecture](), [design-patterns](), [event-driven](), [multi-tenancy](), [saas](), [saas-billing](), [usage-based-billing]() ### AI overview A guide to designing billing systems for multi-tenant SaaS applications. It compares centralized billing services, event-driven pipelines, and external billing platforms, and discusses product catalogs, subscription management, usage metering, tenant isolation, and build-versus-buy decisions. ### Source excerpt Design patterns for building multi-tenant billing systems in SaaS. Covers tenant isolation, metering, event-driven billing, and when to build vs buy billing infrastructure. ## Payment API: How to Accept Payments Programmatically DevFeed: [Payment API: How to Accept Payments Programmatically]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-04-13T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [API](), [Automation](), [event driven](), [SDKs](), [Back end](), [Developer experience]() Tags: [api](), [apis](), [automation](), [backend](), [developer](), [developer-guide](), [guide](), [how-to](), [merchant-of-record](), [payment](), [payments](), [saas](), [subscription](), [usage-based-billing](), [webhooks](), [workflows]() ### AI overview A practical guide to using payment APIs for programmatic checkout, subscriptions, usage-based billing, webhook-driven workflows, and keeping product access synchronized with payment state. ### Source excerpt A practical guide to using a payment API for programmatic checkout, billing automation, webhook handling, and scalable payment workflows. ## How Asynchronous Systems Trade Execution Order for Scale and Availability DevFeed: [How Asynchronous Systems Trade Execution Order for Scale and Availability]() Original publisher: [Read original article]() Author: Raul Junco Published: 2026-03-28T13:04:17Z Content type: article Language: en Sources: [System Design Classroom]() Topics: [systems](), [event driven](), [Availability]() Tags: [async](), [availability](), [consistency](), [event-driven](), [ordering](), [parallel](), [pipelines](), [queue](), [scale](), [state](), [systems]() ### AI overview The article explains how asynchronous systems improve scale, throughput, and availability by processing work in parallel, while making execution order and consistency harder to control. Queues, retries, and parallel consumers can reorder related events, producing incorrect final state even when every component succeeds. ### Source excerpt Many developers only realize it after consistency breaks. ## How Socialpruf built a faster, more reliable data stack by replacing Neon with Postgres managed by ClickHouse DevFeed: [How Socialpruf built a faster, more reliable data stack by replacing Neon with Postgres managed by ClickHouse]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-03-17T12:18:28Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [dashboards](), [data-processing](), [Database](), [Node.js](), [event driven]() Tags: [analytics](), [clickhouse](), [dashboards](), [data](), [data-processing](), [event-driven](), [node](), [postgres]() ### AI overview Socialpruf migrated its analytics workload from Neon to Postgres managed by ClickHouse Cloud. The platform uses ClickHouse for customer-facing social analytics, aggregating millions of rows in milliseconds to power near-instant dashboards, while Node.js and Python components collect and process data from multiple social platforms. ### Source excerpt Socialpruf migrated from Neon to Postgres managed by ClickHouse, eliminating network transfer costs and achieving up to 5x faster query performance while powering real-time social analytics dashboards that aggregate millions of rows in milliseconds. ## Unordered mode for GCS ClickPipes is now available DevFeed: [Unordered mode for GCS ClickPipes is now available]() Original publisher: [Read original article]() Author: Marta Paes Published: 2026-03-16T09:28:23Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Cloud](), [event driven](), [data](), [Amazon S3]() Tags: [clickhouse](), [cloud](), [data](), [event-driven](), [google-cloud](), [s3](), [storage]() ### AI overview ClickHouse Cloud now supports unordered ingestion from Google Cloud Storage through GCS ClickPipes. With Google Cloud Pub/Sub notifications, files can be processed as they arrive regardless of lexicographical order, covering backfills, retries, and late-arriving data. ### Source excerpt You can now ingest data from Google Cloud Storage into ClickHouse Cloud in any order for event-driven, blazing-fast analytics. ## Event-Driven Architecture with Apache Kafka DevFeed: [Event-Driven Architecture with Apache Kafka]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-03-16T00:00:00Z Content type: tutorial Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [event driven](), [Kafka](), [Apache-Kafka](), [Scalability](), [Resilience](), [streaming-data-processing](), [Latency](), [systems]() Tags: [apache-kafka](), [architecture](), [asynchronous](), [consumer](), [distributed-systems](), [event-driven-architecture](), [event-sourcing](), [kafka](), [microservices](), [partitions](), [real-time](), [resilience](), [scalability](), [schemas](), [stream-processing]() ### AI overview This tutorial explains event-driven architecture and how Apache Kafka supports asynchronous, real-time data processing. It covers producers, consumers, immutable events, event sourcing, scalability, resilience, stream-processing pipelines, and challenges such as ordering, debugging, and eventual consistency. ### Source excerpt Event-driven architecture replaces synchronous point-to-point calls with immutable events on a durable log, so producers and consumers scale and fail independently. Kafka provides that log: topics sharded into ordered append-only partitions, replicated across brokers, with consumer groups sharing partitions to scale read throughput. ## System Migration: Minimize Downtime, Maximize Efficiency DevFeed: [System Migration: Minimize Downtime, Maximize Efficiency]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-03-16T00:00:00Z Content type: tutorial Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [migration](), [systems](), [async](), [client](), [event driven](), [Kafka](), [API]() Tags: [apache-kafka](), [api](), [architecture](), [async](), [bridge-layer](), [data-pipelines](), [downtime](), [event-driven-architecture](), [metrics](), [migration](), [observability](), [production](), [reliability](), [rollback](), [system-migration](), [traffic-leakage]() ### AI overview A practical guide to migrating an existing system with minimal disruption. It recommends isolated-environment testing, load testing, adapters for incompatible contracts, synchronized asynchronous pipelines, a Kafka-based shared stream, a bridge layer, staged traffic switching, monitoring, and rollback preparation. ### Source excerpt Migrate behind a bridge layer that routes all client traffic and supports three modes: old-only, dual, and new-only. Run dual mode to compare responses without user impact, keep a back-sync pipeline so the old system stays current for rollback, and shift traffic in stages while watching metrics at each step. [Next page]()