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TITelcoEdge Inc.telcoedgeinc.hashnode.dev6d ago · 9 min read

Kafka vs RabbitMQ for Telecom Event Processing: Which Architecture Fits Better?

Modern telecom platforms generate an enormous number of events. A subscriber activates a service, a SIM changes state, a device establishes a session, a usage record is generated, a payment is complet

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BABetini Akarandutbetiniakarandut.hashnode.devSep 2 · 23 min read

Building Offline-First Systems for Low-Connectivity Environments: Architecture Lessons from Nigerian Agritech

A note on the architecture This article describes lessons from a production agricultural technology platform. Some implementation details, operational parameters, identifiers, and business-specific lo

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PPPrajapati Pareshsmarttechdevs.hashnode.devSep 2 · 5 min read

Backend Architecture: Change Data Capture (CDC)

The Death of the Nightly Cron Job For decades, enterprise data synchronization has relied on a deeply flawed architectural pattern: the batch job. Imagine an e-commerce platform where the primary rela

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SUSandesh Upadhayayengineeringlair.hashnode.devSep 1 · 20 min read

I Built Kafka From Scratch to Understand how it actually works

But Why?? I had used Kafka enough to know the terminology: topics, partitions, producers, consumers, replication. But knowing the pieces is different from understanding why they fit together the way t

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SGSiddhant Gortetechwithsiddhant.hashnode.devAug 31 · 16 min read

Kafka Explained Like You're 5: Events, Partitions, and Consumer Groups

How does a large application process millions of events every day? Think about a food delivery application. Every minute, thousands of things happen: User places an order Restaurant accepts the order

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OOutworkTechoutworktech.hashnode.devAug 31 · 10 min read

Message Queues Explained: When to Use Kafka, RabbitMQ, or SQS

As applications become more complex, handling every operation synchronously can quickly create performance and reliability problems. A user places an order, a payment needs to be processed, an email n

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PKPRINCE KUMARprincekumar-engineer.hashnode.devAug 30 · 27 min read

Kafka Explained Like You're 5: Events, Partitions, and Consumer Groups

How does a large application process millions of events every day? Think about a food delivery app. Every minute, thousands of things are happening: Someone creates an account. Someone places an ord

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MTMRIDUL TIWARImriduliti.hashnode.devAug 29 · 7 min read

When Kafka Hits 100% Disk and the Volume Won't Grow

The alert didn't come from consumer lag. It came from disk — two of three brokers on our production Kafka cluster reporting /data at 100%, with about 20K free on an 850G EBS volume. That's not "we sho

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SPSahil Prashardevwithsahil.hashnode.devAug 26 · 18 min read

Kafka Explained Like You're 5: Events, Partitions, and Consumer Groups

How does a large application process millions of events every day? That question sounds much bigger than it actually is. Imagine an online food delivery application. Someone places an order. Then a lo

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MTMuhammad Tahirmtdeveloper.hashnode.devAug 26 · 13 min read

Orchestrating Distributed AI Agents: Ensuring Data Consistency with Event Sourcing & Kafka

This article was originally published on Muhammad Tahir's Portfolio. Introduction & Industry Context The rapid proliferation of AI agents has ushered in a new era of autonomous workflows, transforming

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#kafka

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Trending tags this week

Kafka vs RabbitMQ for Telecom Event Processing: Which Architecture Fits Better?

Building Offline-First Systems for Low-Connectivity Environments: Architecture Lessons from Nigerian Agritech

Backend Architecture: Change Data Capture (CDC)

I Built Kafka From Scratch to Understand how it actually works

Kafka Explained Like You're 5: Events, Partitions, and Consumer Groups

Message Queues Explained: When to Use Kafka, RabbitMQ, or SQS

Kafka Explained Like You're 5: Events, Partitions, and Consumer Groups

When Kafka Hits 100% Disk and the Volume Won't Grow

Kafka Explained Like You're 5: Events, Partitions, and Consumer Groups

Orchestrating Distributed AI Agents: Ensuring Data Consistency with Event Sourcing & Kafka