Rizwan Basha’s Post

📘 Complete Apache Kafka Guide – From Zero to Mastery! If you want to truly master event streaming and real-time data pipelines, Apache Kafka is the technology you need to know. I’ve compiled a comprehensive Kafka guide book covering everything from core concepts to performance optimization with real-world examples. 🔹 Getting Started with Kafka • What is Kafka? • Topics, Partitions & Replication • Leaders, Followers & Consumer Groups 🔹 Practical Kafka Use Cases • Website activity tracking • Real-time event processing • Message service & log aggregation • Data ingestion & event sourcing 🔹 Performance Optimization • Producer tuning – Ack values, batching, compression • Broker optimization – partition balancing, ISR, retention policies • Consumer optimization – scaling groups, connection strategies 🔹 Kafka Architecture & Internals • Brokers, Logs, Records, Offsets • Producer & Consumer APIs • Zoo Keeper & ISR explained • Server types & scaling considerations 💡 This all-in-one Kafka book is designed to take you from beginner ➝ advanced, ensuring you not only understand the fundamentals but can also optimize Kafka for enterprise-scale workloads. 👉 Explore the full Kafka Guide here: https://lnkd.in/gyjskYZN #ApacheKafka #Kafka #EventStreaming #BigData #DataEngineering #RealTimeData #Streaming #LearningCommunity #HelpingHands #AnshLibrary

Continuing our Kafka discussions… After my last post where I tried to explain what Kafka actually is, I had another interesting chat with my friend Abhishek. This time we went a bit deeper and tried to understand how Kafka actually works behind the scenes — and honestly, once this part clicked, everything started making sense. Here’s what we figured out ⬇️ Producer → the one who sends data. Broker → the server where data actually lives. Topic → think of it like a category (for example: “Orders”, “Payments”). Partition → each topic is split into parts for faster processing. Consumer → the one who reads the data. So when data is produced → it goes to a topic → gets divided into partitions → stored across brokers → and then consumed in real time. That’s how Kafka handles millions of messages every second — simple but powerful. ⚡ Next, we’re planning to explore how Kafka makes sure no data is lost (replication & fault tolerance) — that part seems really interesting. If you’ve ever worked with Kafka or faced challenges setting it up, I’d love to hear your experience! #ApacheKafka #SystemDesign #BigData #DataEngineering #LearningInPublic

Kafka Series – Part 7: Producers — How Data Enters Kafka Producers are clients that publish data to Kafka topics. Efficient producer design is crucial for achieving Kafka’s legendary performance. 🧠 1. Key Responsibilities - Decide which topic and partition to write to. - Handle batching, compression, retries, and acknowledgments. - Optimize for latency vs throughput based on use case. 🪄 2. Partitioning Strategies - 🎲 Round Robin: Evenly distributes load across partitions. - 🧭 Key-based: Ensures order for events with the same key (e.g., userID). - ✍ Custom: Fully controlled by your application. 🚀 3. Why It Matters - Proper configuration avoids bottlenecks. - Good partitioning boosts parallelism and maintains ordering where needed. - Efficient producers = stable clusters. 💡 If you feel I missed something important or explained it differently than you would, please drop your thoughts in the comments — I’d love to learn from your perspective too. #Kafka #KafkaProducers #DataPipelines #Partitioning #HighThroughput #LearnTogether

How Kafka Ensures No Data Is Lost Me and my friend Abhishek were again having one of those random tech discussions, and this time we got stuck on a question — --> What happens if a Kafka server suddenly crashes? Does the data just disappear? That’s when we came across something really interesting — Kafka Replication. Basically, Kafka never trusts a single server with your data. Every message is stored in multiple copies across different brokers. Here’s the simple idea 👇 When a producer sends a message, one broker acts as a leader for that partition. Other brokers keep replicas (followers) of that same data. If the leader goes down, one of the followers automatically becomes the new leader — so data stays safe and flow continues without interruption. It’s like having multiple “save points” in a game — even if one fails, your progress isn’t lost. That’s how Kafka maintains its famous reliability and fault tolerance — data might move, but it never disappears. Next up, we’re planning to explore how Kafka handles millions of messages every second — that part really shows the power of its design. #ApacheKafka #SystemDesign #DataEngineering #BigData #LearningInPublic #TechSimplified

Mastering Apache Kafka – From Basics to Performance Optimization! If you’ve ever worked with real-time data, event-driven systems, or streaming pipelines, you’ve probably heard of Apache Kafka. I’ve compiled a complete beginner-to-advanced guide with concepts, examples, and performance tuning tips to help you become Kafka-ready: 🔹 Kafka Basics – Topics, Partitions, Replication, Brokers, Leaders & Consumer Groups 🔹 Example Use Cases – Website tracking, real-time stream processing, log aggregation, event sourcing 🔹 Producers & Consumers – Ack values, batching, compression & client libraries 🔹 Performance Optimization – Tuning brokers, balancing partitions, ISR (In-Sync Replicas), retention policies 🔹 Kafka Architecture Deep Dive – Logs, offsets, Zoo Keeper, producer/consumer APIs 🔹 Best Practices – Partition distribution, avoiding hardcoding, scaling strategies, server concepts 💡 Whether you’re just starting with Kafka or looking to optimize production systems, this guide gives you a clear roadmap from basics ➝ advanced performance tuning. 👉 Check it out for complete notes & hands-on practices 😁 🧐 👍 : https://lnkd.in/gyjskYZN #ApacheKafka #Kafka #EventStreaming #BigData #DataEngineering #RealTimeData #LearningCommunity #HelpingHands #AnshLibrary

Kafka Series – Part 4: Kafka’s Core Design Principles Kafka isn’t “just fast” by accident — it’s designed with a few core principles that make it incredibly reliable and scalable. Let’s break them down clearly 👇 🧱 1. Durability - Kafka persists data to disk immediately. - Uses a commit log structure for sequential writes (very fast). - Data survives restarts and failures — critical for financial or critical systems. 🌐 2. Scalability - Topics can be split into many partitions and distributed across multiple brokers. - You can scale producers, consumers, and brokers independently. - Horizontal scaling makes it ideal for growing data volumes. 🧭 3. Fault Tolerance - Data is replicated to multiple brokers. - If one broker fails, replicas take over automatically. - No single point of failure when configured correctly. 🚀 4. High Throughput & Low Latency - Sequential disk I/O, zero-copy transfer, batching, and compression. - Capable of handling millions of messages/sec with millisecond latency. These principles are why Kafka became the backbone of real-time data platforms worldwide. 💡 If you feel I missed something important or explained it differently than you would, please drop your thoughts in the comments — I’d love to learn from your perspective too. #Kafka #SystemDesign #ScalableArchitecture #FaultTolerance #RealTimeData #LearnTogether

Tired of Your Data Moving Slower Than Your Team? Learn Apache Kafka and Build Real-Time Systems That Actually Talk to Each Other #kafka https://lnkd.in/dgQMiWV3

One of the hidden gems that I have is my video course on Kafka Connect. I think many underestimate its power. Kafka Connect lets you stream data between Kafka and almost any external system without writing a single line of code. That means real-time syncing with zero custom code. A new change in the source database can be instantly reflected in your destination system! I think this is a super powerful and convenient feature. https://lnkd.in/e6qfGKQy #Kafka #KafkaConnect

🚀 Kafka Streams vs. Kafka Connect — Understanding the Core of Real-Time Data Flow. In every modern data architecture, Apache Kafka sits at the heart of streaming and integration. But two of its key components — Kafka Streams and Kafka Connect — often get mixed up. Here’s the simple difference 👇 🔹 Kafka Streams is the Processor — it handles real-time transformations, aggregations, and analytics inside your applications. 🔹 Kafka Connect is The Connector — it moves data in and out of Kafka seamlessly, linking your ecosystem together. Together, they form the perfect pair: Streams drives the logic, Connect moves the data, powering true real-time flow. 💡 At KLogic, we’re all about helping teams achieve full Kafka observability, performance, and automation, so every data stream stays efficient and intelligent. learn more: https://klogic.io/ #Kafka #StreamingData #KafkaStreams #KafkaConnect #DataIntegration #EventDriven #DataEngineering #RealTimeAnalytics #KLogic

🎙️ We recently co-hosted the Kafka Meetup in Amsterdam, where we spoke on “Making Sense of Kafka Metrics with Agentic Design.” The talk sparked great discussions, especially around why we used Kafka Exporter instead of JMX Exporter, the metrics we tracked, and how our monitoring setup performed in real-world conditions. To dive deeper, we’ve put together a two-part blog series that walks you through: ⚙️ Setting up a complete Kafka monitoring pipeline 📊 Collecting and analyzing metrics with Kafka Exporter 🧠 Building efficient, low-latency observability using Parseable If you’re running Kafka in production and want to go beyond dashboards, this one’s for you. You can find the link to the blog post in the first comment below. #Kafka #Observability #Metrics #Monitoring #Parseable #AgenticDesign