🔥 Kafka Internals Masterclass – Behind the Real-Time Curtain

Inside Kafka: The Distributed Log System That Powers the World’s Data Pipelines

Dive deep into how Kafka delivers high throughput, fault tolerance, and exactly-once semantics by coordinating brokers, partitions, replicas, producers, and consumers in a highly distributed system.

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## 🔍 Target Audience:

• Backend Engineers (2–8 years experience)
• DevOps & Platform Engineers
• Architects designing event-driven systems
• Data Engineers working with real-time pipelines

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## 🗓️ Duration:

3 Hours
(60 mins presentation + 30 mins Q&A / 60 min live debugging / group activity)

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| Time | Segment Title | Key Takeaways |
| ---- | ------------- | ------------- |
| `00:00 – 00:15` | 🚀 Why Kafka is Not Just a Queue | Real-world architectures (LinkedIn, Netflix, Uber). Why Kafka > RabbitMQ/Kinesis, use case fit |
| `00:15 – 00:35` | 🧱 Kafka Core Architecture Deep Dive | Brokers, Partitions, Leaders, ISR, Topics, Replication Factor, durability through logs |
| `00:35 – 01:00` | 🔄 Producer Internals & Optimizations | Idempotent producers, acks (0, 1, all), retries, batching, partitioning logic, compression |
| `01:00 – 01:20` | 🔁 Consumer Internals & Group Mechanics | Offset tracking, rebalancing, consumer groups, commit strategies, consumer lag |
| `01:20 – 01:40` | 🔐 Exactly-Once Semantics & Transactions | How EOS works: transactional producer + offset commit in same tx; fencing, idempotency |
| `01:40 – 02:00` | 🧠 Broker Metadata, Controller, Zookeeper/KRaft | Role of the controller node, leadership election, metadata sync, Zookeeper vs. KRaft (Raft Protocol) |
| `02:00 – 02:15` | ☕ Break + Networking | Informal discussion and mini-poll on Kafka usage in attendee projects |
| `02:15 – 02:35` | 🔍 Kafka Failure Modes & Debugging Lessons | Leader re-election, partition under-replication, consumer lag, ISR shrink, backpressure handling |
| `02:35 – 02:55` | 📏 Tuning Kafka at Scale (Throughput vs Durability) | Message size, batch settings, disk throughput, segment size, flush settings |
| `02:55 – 03:10` | 🛠️ Live Demo: Producer Crash with Exactly-Once | Simulate crash + restart of producer with transactions enabled; verify no duplicate consumption |
| `03:10 – 03:30` | 💬 Q&A / System Design Scenarios + Quiz | “What would happen if…” network partition, controller fails, Kafka Streams for microservice state |

## 🧪 Live Demo :

“Exactly-Once Delivery in Action”

• Producer sends transactional messages
• Consumer processes with offset commit in same transaction
• Simulate crash & recovery
• Show how Kafka ensures no duplicate processing

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## 📦 What Participants Will Walk Away With:

• Mastery over Kafka’s internals and distributed log behavior
• Understanding of reliability vs performance trade-offs
• Practical knowledge of designing and debugging Kafka pipelines
• GitHub repo with example code and troubleshooting tips

Join Zoom Meeting

[https://us02web.zoom.us/j/85965034762?pwd=U6IlO7JoRt7b0YKUkYYSfqagLOeG87.1](https://www.google.com/url?q=https://us02web.zoom.us/j/85965034762?pwd%3DU6IlO7JoRt7b0YKUkYYSfqagLOeG87.1&sa=D&source=calendar&usd=2&usg=AOvVaw0PcWrI9mslRJHKSCNn9I_N)

Meeting ID: 859 6503 4762
Passcode: 259291

## 🔧 Tools & Setup (if you want to demo):

• Kafka with KRaft or Zookeeper mode on Docker Compose
• Python/Java producer/consumer clients
• Monitoring with Kafka UI, KPow, or AKHQ
• Simulated failures using `tc` or stopping brokers

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##

> 💥 “Not just a messaging system – Discover the engine behind real-time platforms!”
> 🧠 “Kafka Internals – From Producers to Partitions to Perfect Delivery”
> 📡 “Master the Distributed Log That Feeds the Internet”