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Kafka Architecture & Key
Components
High-Throughput, Real-Time Messaging Platform
What is Apache Kafka?
An open-source distributed event streaming platform
Built for real-time data pipelines and streaming applications
Handles high volume, low-latency data streams
Originally developed by LinkedIn, now part of the Apache Software
Foundation
Why Use Kafka?
Decouples data producers and consumers
Scales horizontally (add more brokers)
Highly durable and fault-tolerant
Common use cases:
Logging and monitoring
Real-time analytics
Stream processing
Event sourcing
Kafka Architecture Overview
Producer → Sends messages
Broker → Kafka server that stores messages
Consumer → Reads messages
Topic → Named channel for messages
Partition → Unit of parallelism in a topic
Messages are stored in partitions, replicated for durability
Key Components of Kafka
Producer – Publishes data to Kafka topics
Broker – Kafka server that receives and stores messages
Consumer – Subscribes to topics and processes messages
Topics & Partitions – Organize messages and enable scaling
Zookeeper – Coordinates and manages brokers (being replaced by KRaft)
]
Kafka Topics & Partitions
Topics: Logical channels where messages are published
Partitions:
Allow parallel processing
Each partition is ordered
Kafka guarantees message order within a partition
Replication provides fault tolerance
Kafka Data Flow (Diagram)
[Producer] → [Topic: my-topic]
|→ [Partition 0] → Broker 1
|→ [Partition 1] → Broker 2
|→ [Partition 2] → Broker 3
↓
[Consumer Group]
Consumers in the same group divide partitions
Each consumer gets a portion of the workload
Contact & Online Training
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✅Hands-on Training with Real-World Use Cases
✅Live Sessions with Industry Experts
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apache kafka training online | kafka online training

  • 1. Kafka Architecture & Key Components High-Throughput, Real-Time Messaging Platform
  • 2. What is Apache Kafka? An open-source distributed event streaming platform Built for real-time data pipelines and streaming applications Handles high volume, low-latency data streams Originally developed by LinkedIn, now part of the Apache Software Foundation
  • 3. Why Use Kafka? Decouples data producers and consumers Scales horizontally (add more brokers) Highly durable and fault-tolerant Common use cases: Logging and monitoring Real-time analytics Stream processing Event sourcing
  • 4. Kafka Architecture Overview Producer → Sends messages Broker → Kafka server that stores messages Consumer → Reads messages Topic → Named channel for messages Partition → Unit of parallelism in a topic Messages are stored in partitions, replicated for durability
  • 5. Key Components of Kafka Producer – Publishes data to Kafka topics Broker – Kafka server that receives and stores messages Consumer – Subscribes to topics and processes messages Topics & Partitions – Organize messages and enable scaling Zookeeper – Coordinates and manages brokers (being replaced by KRaft) ]
  • 6. Kafka Topics & Partitions Topics: Logical channels where messages are published Partitions: Allow parallel processing Each partition is ordered Kafka guarantees message order within a partition Replication provides fault tolerance
  • 7. Kafka Data Flow (Diagram) [Producer] → [Topic: my-topic] |→ [Partition 0] → Broker 1 |→ [Partition 1] → Broker 2 |→ [Partition 2] → Broker 3 ↓ [Consumer Group] Consumers in the same group divide partitions Each consumer gets a portion of the workload
  • 8. Contact & Online Training 📢We Provide Online Training on Databricks and Big Data Technologies! ✅Hands-on Training with Real-World Use Cases ✅Live Sessions with Industry Experts ✅Certification Guidance 🌐Visit our website: https://www.accentfuture.com/ 📩For inquiries, contact us at: [email protected]