Apache KAFKA in One Video 🔥 With Practical Examples | For Beginners [HINDI]

Title: 🔥 Apache Kafka Explained with Real-World Examples + Hands-On Projects!

📌 Description:
Welcome to this comprehensive Apache Kafka tutorial! 🚀 Whether you're a beginner or an experienced developer, this video will help you master Kafka with real-world examples, in-depth theory, and practical hands-on projects.

👉 What You’ll Learn in This Video:
✅ Why was Kafka Created? The reason behind its inception (02:48)
✅ Kafka in Uber: How Uber uses Kafka for real-time data streaming (06:17)
✅ Kafka Benefits: Why it's the best choice for messaging systems (08:04)
✅ Kafka Core Concepts: Brokers, Topics, Partitions, Offsets & More (09:08 - 22:28)
✅ Kafka Architecture: Clusters, Zookeeper vs. KRaft, and more (22:28 - 32:37)
✅ Kafka Hands-On Practical Implementation (32:37 - 57:22)
✅ Kafka in Different OS Environments (Windows, Mac, Linux)
✅ Advanced Kafka Projects: Python, Kafka UI, Consumer Groups, & More! (1:20:05 - 1:50:46)

Slides link
https://www.canva.com/design/DAGPEHV8...

🔥 Hands-On Practical Demos:
✔️ Running Kafka & Zookeeper on Windows, Mac, & Linux
✔️ Kafka Producer & Consumer in Action
✔️ Kafka with Multiple Partitions & Consumer Groups
✔️ Real-World Project: Kafka with Python

📌 Why Watch This Video?
✔️ Easy to Follow – Step-by-step explanations for beginners & pros
✔️ Real-World Applications – Learn how big tech (Uber, LinkedIn) uses Kafka
✔️ Hands-on Coding & Projects – Not just theory, but practical implementation

💬 Timestamps for Easy Navigation:
⏳ Jump to any topic using the timestamps in the video chapters!

🚀 🔔 Don't forget to LIKE, SUBSCRIBE & COMMENT if you find this video helpful!
📢 Share this video with fellow developers & data engineers!

🔗 #Kafka #ApacheKafka #KafkaTutorial #BigData #Streaming #DataEngineering #KafkaForBeginners #KafkaWithPython #KafkaWithJava

TimeLines
00:00 Intro
02:48 Why was Kafka Created?
06:17 Kafka in Uber - Overview
08:04 Benefits of Kafka
09:08 Kafka key Components
09:20 What is Kafka Broker?
09:40 What is Kafka Topics?
10:45 Kafka Producer & Consumer
12:45 Kafka Partitions
13:58 Kafka Offsets
15:57 Kafka Message Keys
18:49 Kafka Serialization
19:21 Kafka Consumer Groups
22:28 Kafka Cluster
28:32 Kafka Zookeeper
31:38 Kafka KRaft
32:37 Kafka Practical Overview
33:22 JAVA On Windows
37:11 JAVA On MAC
40:56 JAVA On Linux
44:13 Run Kafka & Zookeeper On Windows
48:38 Run Kafka & Zookeeper On MAC & Linux
52:57 Running Kafka with KRaft
00:57:22 Practical1 - Kafka with Producer & Consumer
01:05:10 Practical2 - Kafka with Multiple Partitions & Consumers
01:20:05 Practical3 - Kafka with Multiple Consumer Groups
01:23:17 Kafka UI Dashboard
01:35:16 Project - Kafka with Python
01:46:58 What is Kafka Connect?
01:50:46 What is Kafka Streams?



Theory
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✅ Kafka Core Concepts: Brokers, Topics, Partitions, Offsets & More

🟢 Kafka Broker
A Kafka broker is a server that stores and delivers messages.
A Kafka cluster consists of multiple brokers for load balancing & fault tolerance.
Brokers store messages in topics and handle consumer-producer interactions.

🟢 Kafka Topics
Topics are logical categories where messages are published.
A producer sends messages to a topic, and consumers subscribe to it.
Topics can have multiple partitions to improve parallel processing.

🟢 Kafka Producer & Consumer
Producers send data to Kafka topics.
Consumers read data from topics asynchronously.
Consumers subscribe to topics and process messages in real time.
Kafka provides consumer groups to distribute load among multiple consumers.

🟢 Kafka Partitions
Kafka splits topics into multiple partitions to scale horizontally.
Each partition is stored on different brokers, allowing parallel processing.
Data is ordered within a partition but unordered across partitions.

🟢 Kafka Offsets
Offsets track message positions within a partition.
Each message has a unique offset number to ensure consumers read messages sequentially.

🟢 Kafka Message Keys
Kafka message keys decide which partition a message is stored in.

🟢 Kafka Serialization
Messages in Kafka are stored as bytes.
Serialization formats: JSON, Avro (efficient for schema evolution), Protocol Buffers.

🟢 Kafka Consumer Groups
Multiple consumers can read from the same topic in parallel.
Consumers in the same group share partitions for better load balancing.
Each partition is assigned to only one consumer per group.

🟢 Kafka Cluster
A Kafka cluster consists of multiple brokers working together.
Clusters provide high availability & fault tolerance.
Data is replicated across multiple brokers for reliability.

🟢 Kafka Zookeeper
Zookeeper manages metadata and leader election in Kafka.
Stores broker information, partition leader details, and consumer offsets.

🟢 Kafka KRaft (Kafka Raft)
KRaft (Kafka’s built-in consensus protocol) replaces Zookeeper.