Apache Kafka – Stream Processing Course in Chennai

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Master Apache Kafka Through Real-Time Stream Processing Projects

Edubrights offers industry-focused Apache Kafka – Stream Processing Training in Chennai designed for students, freshers, data engineers, big data developers, backend developers, software engineers, DevOps engineers, cloud professionals, and working professionals seeking expertise in real-time data streaming and event-driven architecture. Learn how to build scalable, fault-tolerant, and high-performance data pipelines using Apache Kafka.

Gain practical experience in Apache Kafka architecture, producers, consumers, topics, partitions, brokers, Kafka Connect, Kafka Streams, event streaming, message queues, real-time data pipelines, stream processing, Apache ZooKeeper, cluster management, monitoring, performance tuning, and real-world Apache Kafka implementation projects through expert-led training and hands-on labs.

Why Choose Edubrights for Apache Kafka Training?

✅ Real-Time Stream Processing Projects & Industry Case Studies
✅ Live Instructor-Led Training by Big Data Experts
✅ Hands-On Practice with Apache Kafka & Kafka Streams
✅ Event-Driven Architecture & Data Pipeline Development Experience
✅ Certification Preparation & Technical Guidance
✅ Resume Building & Mock Interview Preparation
✅ Career Guidance & Job Assistance
✅ Flexible Online, Classroom & Weekend Training Options

Build In-Demand Stream Processing Skills

Learn how leading organizations use Apache Kafka to build real-time data pipelines, process event streams, integrate distributed systems, support microservices, and enable scalable data processing across modern cloud and big data environments. Our training combines practical implementation, event streaming concepts, and industry best practices to help you become job-ready.

Start Your Apache Kafka Career Today

Join Edubrights' Apache Kafka – Stream Processing Course in Chennai and gain the skills required to build real-time streaming applications, manage event-driven architectures, develop scalable data pipelines, and build a successful career in Big Data Engineering, Data Streaming, and Cloud Computing.

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₹38000

₹42000

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Case Studies and Projects

Hours of Training

20+

Placement Assurance

100%

Expert Support

yes

Support & Access

Lifetime

Certification

Yes

Skill Level

All

Language

All

Course Objectives

1. What will you learn in the Apache Kafka course?

You will learn how to build, manage, and scale real-time event streaming applications using Apache Kafka. The course covers Kafka architecture, producers, consumers, topics, partitions, brokers, Kafka Connect, Kafka Streams, replication, security, monitoring, and performance optimization.

2. How does this course prepare you for real-world data streaming projects?

The course provides hands-on experience in developing event-driven applications, building real-time data pipelines, integrating Kafka with databases and cloud platforms, processing streaming data, and implementing fault-tolerant messaging systems.

3. What practical skills will you develop during the training?

You will gain expertise in Kafka installation, cluster configuration, topic management, producer and consumer development, event streaming, Kafka Connect, Kafka Streams, monitoring, security, replication, and high availability.

4. How will real-time projects improve your Apache Kafka skills?

You will work on practical projects involving financial transaction processing, log aggregation, IoT data streaming, customer activity tracking, microservices communication, and real-time analytics platforms.

5. What career opportunities can you pursue after learning Apache Kafka?

This course prepares you for roles such as Data Engineer, Big Data Engineer, Kafka Developer, Streaming Data Engineer, Backend Developer, Cloud Data Engineer, DevOps Engineer, and Software Engineer.

Popular Techniques Covered in This Course

Apache Kafka Architecture and Components

Kafka Producers and Consumers

Topics, Partitions, and Brokers

Kafka Connect and Source/Sink Connectors

Kafka Streams for Real-Time Processing

Event-Driven Architecture and Messaging

Cluster Configuration and Replication

Monitoring, Logging, and Performance Tuning

Security, Authentication, and Authorization

Integration with Spark, Flink, Hadoop, and Cloud Platforms

Get Hands-on Knowledge about Real-Time Projects

Project 1

Project 1: Real-Time Banking Transaction Processing

Develop a Kafka-based event streaming platform to process financial transactions with high availability and low latency.

Project 2

Project 2: IoT Sensor Data Streaming

Build a streaming application that collects and processes real-time IoT sensor data for monitoring and analytics.

Project 3

Project 3: Log Aggregation and Monitoring System

Create a centralized log processing solution using Kafka to collect, stream, and analyze application and server logs.

Project 4

Project 4: E-Commerce Event Streaming Platform

Develop an event-driven architecture that streams customer activity, order processing, and inventory updates in real time.

Project 5

Project 5: Real-Time Analytics Pipeline

Build an end-to-end streaming data pipeline using Apache Kafka integrated with Spark or Flink to deliver live business insights.

Key Features

Edubrights offers Apache Kafka – Stream Processing Training in virtual mode with expert trainers. Here are the key features,

40 Hours Course Duration

100% Job Oriented Training

Industry Expert Faculties

Free Demo Class Available

Completed 500+ batches

Certification Guidance

Curriculum

Module 1: Kafka Architecture & Concepts

Event streaming vs traditional messaging
Kafka components: broker, topic, partition, offset
Kafka cluster: ZooKeeper and KRaft mode
Replication: leader, follower, ISR
Kafka vs RabbitMQ vs Pulsar: comparison
Kafka ecosystem: Confluent Platform overview

Module 2: Producers

Kafka Java Producer API
ProducerRecord: key, value, partition, timestamp
Acknowledgement modes: acks=0, 1, all
Serialisers: String, JSON, Avro
Producer idempotence and exactly-once semantics
Batching, linger.ms, and compression

Module 3: Consumers

Consumer group and partition assignment
Poll loop and offset management
Auto vs manual offset commit
Rebalance: eager and cooperative strategies
Consumer lag monitoring
Deserialisation and schema validation

Module 4: Kafka Topics & Administration

Topic creation: partitions, replication factor
Retention: time-based and size-based
Compacted topics for state stores
Admin API: programmatic topic management
kafka-topics.sh and kafka-console tools
Topic monitoring with Confluent Control Center

Module 5: Schema Registry & Avro

Schema evolution: backward and forward compatibility
Avro schema definition and serialisation
Confluent Schema Registry setup and usage
Schema versioning and lifecycle
Protobuf and JSON Schema support
Schema validation in CI/CD pipelines

Module 6: Kafka Streams

Streams DSL: KStream, KTable, GlobalKTable
Stateless operations: filter, map, flatMap
Stateful operations: groupBy, aggregate, windowing
Join operations: stream-stream and stream-table
State stores: in-memory and RocksDB-backed
Topology testing with TopologyTestDriver

Module 7: Kafka Connect

Kafka Connect framework: source and sink connectors
JDBC Source and Sink connectors
S3 Sink connector for data lake integration
Debezium CDC (Change Data Capture) connector
Single Message Transforms (SMTs)
Kafka Connect cluster deployment and management

Module 8: Operations & Production

Broker configuration tuning: performance and reliability
Monitoring: JMX metrics and Grafana dashboards
Kafka security: SASL/PLAIN, TLS, ACL
Disaster recovery: MirrorMaker 2 replication
Confluent Cloud: managed Kafka
Kafka in microservices architecture patterns

Receive Training From Our Skilled and Effective Trainers

Our Apache streaming instructors are senior data engineers and platform architects who have built production Kafka and Spark pipelines processing billions of events daily for e-commerce, fintech, and media streaming companies. They teach real-world fault-tolerant design patterns, operational best practices, and performance tuning from live cluster experience.

Professional Apache Kafka Event Streaming Certification

The Professional Apache Kafka Event Streaming Certification validates your expertise in building scalable event-driven applications and real-time data streaming solutions using Apache Kafka.

Course FAQs

1. What is Apache Kafka?

Answer: Apache Kafka is an open-source distributed event streaming platform used for building real-time data pipelines and event-driven applications.

2. What is a Kafka Topic?

Answer: A Kafka Topic is a logical channel where producers publish messages and consumers subscribe to receive them.

3. What is a Kafka Producer?

Answer: A Kafka Producer is an application that sends messages or events to Kafka topics.

4. What is a Kafka Consumer?

Answer: A Kafka Consumer reads and processes messages from Kafka topics for downstream applications.

5. Why is Apache Kafka used?

Answer: Apache Kafka is used for real-time messaging, event streaming, log aggregation, data integration, and scalable communication between distributed applications.

6. Which industries use Apache Kafka?

Answer: Banking, finance, healthcare, retail, telecommunications, manufacturing, e-commerce, logistics, cloud computing, and enterprise software companies use Apache Kafka.

7. Is Apache Kafka suitable for enterprise applications?

Answer: Yes. Apache Kafka is widely used for mission-critical, high-throughput, fault-tolerant, and scalable enterprise event streaming systems.

8. Does the course include practical Kafka projects?

Answer: Yes. The course includes hands-on projects involving event streaming, IoT systems, real-time analytics, log processing, and enterprise messaging.

9. Is Apache Kafka beginner-friendly?

Answer: Yes. Learners with basic Java or Python programming knowledge can understand Kafka through structured practical training.

10. Why should professionals learn Apache Kafka?

Answer: Apache Kafka is one of the industry's leading event streaming platforms and is a highly sought-after skill in big data, cloud computing, and microservices development.