By the end of this course, learners will be able to:

1. Understand real-time data streaming concepts and the role of Apache Kafka in modern architectures.
2. Explore the core components of Kafka: Brokers, Topics, Partitions, Producers, and Consumers.
3. Set up and configure Apache Kafka clusters for high availability and performance.
4. Build producers and consumers using Kafka’s APIs for real-time message publishing and consumption.
5. Implement Kafka Connect for integrating with external systems (databases, cloud services, etc.).
6. Use Kafka Streams and KSQL for building data transformation pipelines and analytics.
7. Design fault-tolerant and scalable data streaming architectures.
8. Monitor and manage Kafka clusters using tools and metrics.
9. Apply security and best practices, including encryption, authentication, and topic-level access control.

Apache Kafka - Course Syllabus

1. Introduction to Apache Kafka

• Overview of Apache Kafka and its architecture
• Understanding Kafka topics, partitions, and brokers
• Use cases and applications of Kafka in real-time data streaming

2. Setting up Apache Kafka

• Installing and configuring Apache Kafka clusters
• Managing topics, partitions, and replication in Kafka
• Monitoring and managing Kafka clusters using command-line tools and web interfaces

3. Kafka Producers and Consumers

• Writing Kafka producers to publish messages to topics
• Developing Kafka consumers to subscribe to topics and process messages
• Configuring producers and consumers for high throughput and fault tolerance

4. Kafka Connect: Integrating with External Systems

• Introduction to Kafka Connect framework
• Building and deploying Kafka connectors for integrating with external data sources and sinks
• Configuring connectors for various use cases such as databases, message queues, and file systems

5. Kafka Streams: Stream Processing with Kafka

• Introduction to Kafka Streams library
• Developing stream processing applications using Kafka Streams DSL
• Implementing real-time data transformation, aggregation, and analytics with Kafka Streams

6. Advanced Kafka Concepts

• Kafka architecture patterns and best practices
• Security and authentication in Kafka clusters
• Performance tuning and optimization techniques for Kafka deployments

7. Real-world Kafka Applications and Use Cases

• Case studies and examples of real-world Kafka deployments
• Building end-to-end streaming applications with Kafka for use cases such as log aggregation, event-driven architectures, and IoT data processing

8. Monitoring and Operations

• Monitoring Kafka clusters and applications using metrics and logging
• Performing maintenance tasks such as scaling, upgrading, and reconfiguring Kafka clusters
• Handling common operational challenges and troubleshooting issues in Kafka deployments

9. Best Practices and Optimization

• Best practices for designing, deploying, and managing Kafka clusters
• Optimization techniques for improving Kafka performance, scalability, and reliability
• Implementing disaster recovery and high availability strategies for Kafka deployments

10. Hands-on Projects and Labs

• Hands-on exercises and projects applying learned concepts and techniques
• Building real-time data streaming applications using Kafka
• Implementing end-to-end data pipelines with Kafka for various use cases

11. Final Project and Certification

• Capstone project demonstrating mastery of Apache Kafka concepts and skills
• Evaluation and feedback from instructors and peers
• Course completion certificate for successful participants

This syllabus covers a comprehensive range of topics to equip participants with the knowledge, skills, and practical experience needed to design, deploy, and manage real-time data streaming applications using Apache Kafka.

Upon completing the Apache Kafka Essentials: Mastering Real-Time Data Streaming course, you’ll receive a Course Completion Certificate from Uplatz, validating your ability to design and manage streaming data pipelines using Kafka.

This certificate strengthens your professional profile and positions you for roles in big data, data engineering, DevOps, and stream analytics. It also serves as a solid foundation for pursuing Confluent Certified Developer for Apache Kafka (CCDAK) or similar certifications.

Apache Kafka is a key technology in modern data-driven organizations. After completing this course, you can pursue roles such as:

1. Apache Kafka Developer
2. Real-Time Data Engineer
3. Streaming Data Architect
4. Big Data Engineer
5. DevOps Engineer (Streaming Focus)
6. Data Integration Specialist
   
   

Kafka skills are in high demand across industries such as finance, e-commerce, logistics, telecommunications, IoT, and cloud computing, where real-time data processing is essential for business intelligence, automation, and user experience.

1. What is Apache Kafka and what is it used for?
Apache Kafka is a distributed streaming platform used for building real-time data pipelines and stream processing applications. It’s designed for scalability, fault-tolerance, and high throughput.

2. What are Kafka Topics and Partitions?
A Topic is a logical stream of messages in Kafka. Each topic is split into partitions, which allow Kafka to scale horizontally and process messages in parallel.

3. How does a Kafka Producer work?
A Kafka Producer sends messages to a Kafka topic. It can choose partitions explicitly or rely on Kafka’s built-in partitioning logic based on keys or round-robin.

4. What is the role of a Kafka Consumer?
Consumers read messages from Kafka topics. They can be organized into consumer groups for load balancing and parallel processing of data.

5. What is Kafka Connect?
Kafka Connect is a framework for connecting Kafka with external systems such as databases and cloud services using ready-made or custom connectors.

6. What are Kafka Streams and how do they differ from Kafka Connect?
Kafka Streams is a client library for real-time data transformation and aggregation. Unlike Kafka Connect (integration-focused), Streams is used to process data in-flight.

7. How does Kafka ensure durability and reliability of messages?Kafka writes data to disk, replicates it across brokers, and allows configuration of acknowledgment levels to ensure message durability and delivery guarantees.

8. What are the different delivery semantics in Kafka?
Kafka supports at-most-once, at-least-once, and exactly-once delivery semantics, depending on producer and consumer configuration.

9. What tools can be used to monitor Kafka clusters?
Common tools include Confluent Control Center, Kafka Manager, Prometheus, Grafana, and JMX-based tools.

10. How does Kafka handle message retention?
Kafka retains messages for a configurable time or size limit, even after they’ve been consumed, allowing for reprocessing or fault recovery.

1. What is Apache Kafka?
Apache Kafka is a high-performance distributed messaging system for real-time data streaming and processing.

2. Who should take this course?
Ideal for software developers, data engineers, DevOps professionals, and system architects working on or planning to build real-time data pipelines.

3. Is this course suitable for beginners?
Yes, it begins with Kafka fundamentals and gradually covers intermediate to advanced concepts with practical examples.

4. What is the format of the course?
Self-paced with video lectures, live demos, code samples, and exercises.

5. Will I receive a certificate?
Yes, you’ll receive a Course Completion Certificate from Uplatz upon successful completion.