InfoQ Homepage Event Driven Architecture Content on InfoQ
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Introducing the RIG Model - the Puzzle of Designing Guaranteed Data-Consistent Microservice Systems
The RIG model formulates three rules for a saga call chain. Using a gamified RIG tool, consisting of three main RIG puzzle pieces, teams can model a microservice system that guarantees eventual data consistency.
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Building Kafka Event-Driven Applications with KafkaFlow
KafkaFlow, a .NET open-source project, simplifies Kafka-based event-driven app development with features like middleware for message processing, enhancing maintainability, customization potential, and allowing developers to prioritize business logic.
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A Comprehensive Guide to Building Event-Driven Architecture on Azure, AWS, and Google Cloud
In this article, you'll find guidance to Azure, AWS, and Google Cloud resources, along with unique architecture examples that incorporate the AWS EventBridge, SNS, Azure Service Bus, Eventgrid, and Google Cloud Eventarc. These examples can help you better grasp the resources’ concepts and enable you to kickstart building your own architecture using an event-driven approach.
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A Case for Event-Driven Architecture with Mediator Topology
This article tells the story about a business case using Event-Driven Architecture with Mediator topology and an implementation that provided elastic scalability, reliability, and durable workflows. All were built using Kubernetes, KEDA, AWS, and .NET technologies.
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Tales of Kafka at Cloudflare: Lessons Learnt on the Way to 1 Trillion Messages
Cloudflare uses Kafka clusters to decouple microservices and communicate the creation, change or deletion of various resources via protobuf, a common data format in a fault-tolerant manner. The authors suggest investing in metrics for problem detection, prioritizing clear SDK documentation, and balancing flexibility and simplicity for standardized pipelines.
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Billions of Messages Per Minute Over TCP/IP
Chronicle Wire offers an alternative way of transferring data between systems, delivering more messages, faster, than common JSON/XML approaches. This approach to data serialization improves both latency and throughput.
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Migrating Netflix's Viewing History from Synchronous Request-Response to Async Events
In a web-based service, a slowdown in request processing can eventually make your service unavailable. Chances are, not all requests need to be processed right away. Some of them just need an acknowledgement of receipt. Have you ever asked yourself: “Would I benefit from asynchronous processing of requests? If so, how would I make such a change in a live, large-scale mission critical system?”
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Streaming-First Infrastructure for Real-Time Machine Learning
This article covers the benefits of streaming-first infrastructure for two scenarios of real-time ML: online prediction, where a model can receive a request and make predictions as soon as the request arrives, and continual learning, when machine learning models are capable of continually adapting to change in data distributions in production.
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How to Create a Network Proxy Using Stream Processor Pipy
In this article we are going to introduce Pipy, an open-source cloud-native network stream processor. After describing its modular design, we will see how to rapidly build a high-performance network proxy to serve our specific needs. Pipy has been battle-tested and is already in use by multiple commercial clients.
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Turning Microservices Inside-Out
Turning microservices inside-out means moving past a single, request/response API to designing microservices with an inbound API for queries and commands, an outbound APIs to emit events, and a meta API to describe them both. A database can be supplemented with Apache Kafka via a connecting tissue such as Debezium.
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Beyond the Database, and beyond the Stream Processor: What's the Next Step for Data Management?
Databases have been around forever with the same shape: you make a request to your data and then you receive an answer. Now, stream processors came along with a different approach: data isn’t locked up, it is in motion. Understand how stream processors and databases relate and why there is an emerging new category of databases that focus on data that stays in place as well as data that moves.
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Real Time APIs in the Context of Apache Kafka
Events offer a Goldilocks-style approach in which real-time APIs can be used as the foundation for applications which is flexible yet performant; loosely-coupled yet efficient. Apache Kafka offers a scalable event streaming platform with which you can build applications around the powerful concept of events.