Apache Kafka for Data Engineers Training Course

 Apache Kafka for Data Engineers Training Course 

Introduction 

Apache Kafka for Data Engineers Training Course is designed to equip participants with practical skills and advanced technical knowledge in distributed event streaming, real-time data processing, and scalable data engineering. Apache Kafka is a leading open-source distributed event streaming platform used to build high-performance data pipelines, event-driven architectures, real-time analytics systems, and enterprise data integration solutions. This course explores Kafka architecture, cluster management, topics, partitions, producers, consumers, brokers, replication, and fault tolerance. Participants will gain practical experience in designing reliable data streaming infrastructures that support big data analytics, cloud computing, microservices, and modern data platforms. 

The training further focuses on advanced Apache Kafka implementation, including Kafka Connect, Kafka Streams, schema management, stream processing, data integration, security, and performance optimization. Through practical exercises and global industry case studies, participants will learn how organizations such as LinkedIn, Netflix, and Uber leverage event streaming technologies to manage large-scale data flows and real-time applications. The course also examines data governance, monitoring, disaster recovery, and scalable pipeline development, enabling data engineers to build production-ready streaming solutions. By the end of the training, participants will be equipped to implement enterprise-grade Apache Kafka solutions that improve data accessibility, operational efficiency, system integration, and real-time decision-making. 

Course Objectives 

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

1. Understand Apache Kafka architecture and distributed event streaming fundamentals.
2. Design scalable real-time data engineering pipelines.
3. Configure Kafka brokers, clusters, topics, and partitions.
4. Implement reliable Kafka producers and consumers.
5. Apply replication and fault-tolerance mechanisms.
6. Develop real-time data integration solutions.
7. Implement Kafka Connect for enterprise data integration.
8. Apply Kafka Streams for real-time stream processing.
9. Integrate Kafka with big data and cloud platforms.
10. Implement data serialization and schema management.
11. Strengthen Kafka security and data governance.
12. Monitor, troubleshoot, and optimize Kafka performance.
13. Design resilient event-driven data architectures.

Organizational Benefits
 

1. Improved real-time data processing and analytics capabilities.
2. Enhanced enterprise data integration and accessibility.
3. Reduced data pipeline latency and operational bottlenecks.
4. Improved scalability of data infrastructure.
5. Stronger data reliability and fault tolerance.
6. Enhanced operational monitoring and performance management.
7. Improved support for cloud-based data engineering.
8. Reduced complexity in enterprise system integration.
9. Better data-driven decision-making.
10. Increased organizational readiness for digital transformation.

Target Audiences
 

1. Data engineers and big data professionals.
2. Data architects and enterprise architects.
3. Database administrators and system administrators.
4. Software developers and backend engineers.
5. Cloud computing and DevOps engineers.
6. Business intelligence and analytics professionals.
7. IT infrastructure and integration specialists.
8. Technology consultants and data platform managers.

Course Duration: 5 days

Course Modules

Module 1: Introduction to Apache Kafka and Event Streaming
 

• Distributed event streaming concepts and applications.
• Apache Kafka architecture and core components.
• Topics, partitions, brokers, and messages.
• Event-driven architecture and microservices integration.
• Kafka ecosystem and enterprise applications.
• Case study: LinkedIn's adoption of Kafka for large-scale activity data streaming.

Module 2: Kafka Cluster Architecture and Administration
 

• Kafka broker configuration and cluster deployment.
• ZooKeeper architecture and KRaft mode.
• Partition leadership and replication management.
• Cluster availability and fault tolerance.
• Broker monitoring and administration.
• Case study: Netflix-inspired distributed streaming infrastructure for high-volume data delivery.

Module 3: Kafka Producers and Consumers
 

• Producer configuration and message publishing.
• Consumer groups and offset management.
• Message delivery semantics and acknowledgements.
• Consumer rebalancing and partition assignment.
• Error handling and reliable message processing.
• Case study: Uber's event-driven data pipelines for real-time transportation services.

Module 4: Kafka Topics, Partitions and Data Management
 

• Topic creation and configuration management.
• Partitioning strategies and message ordering.
• Replication factors and retention policies.
• Log compaction and data lifecycle management.
• Data throughput and partition optimization.
• Case study: LinkedIn's distributed event storage and data management architecture.

Module 5: Kafka Connect and Enterprise Data Integration
 

• Kafka Connect architecture and components.
• Source and sink connectors configuration.
• Database integration using change data capture.
• Integrating Kafka with cloud storage and databases.
• Connector monitoring and troubleshooting.
• Case study: Walmart-inspired retail data integration using real-time inventory events.

Module 6: Kafka Streams and Real-Time Data Processing
 

• Kafka Streams architecture and applications.
• Stream processing and transformation techniques.
• Stateful and stateless processing.
• Windowing, joins, and aggregations.
• Real-time analytics and event processing.
• Case study: Financial services applications using Kafka Streams for fraud detection.

Module 7: Kafka Security, Monitoring and Performance Optimization
 

• Authentication and authorization mechanisms.
• Access control lists and encryption.
• Kafka monitoring using operational metrics.
• Performance tuning and resource optimization.
• Troubleshooting and disaster recovery.
• Case study: Enterprise banking systems implementing secure, resilient event streaming.

Module 8: Advanced Kafka Implementation and Practical Project
 

• Kafka integration with Apache Spark and Hadoop.
• Schema Registry and data serialization.
• Avro, JSON, and Protocol Buffers.
• Building end-to-end real-time data pipelines.
• Production deployment and scalability considerations.
• Case study: A global e-commerce real-time order processing and customer analytics platform.

Training Methodology
 

• Instructor-led presentations and technical demonstrations.
• Hands-on Apache Kafka configuration and implementation exercises.
• Practical development of producers, consumers, and streaming applications.
• Group discussions and real-world enterprise case studies.
• Guided laboratory sessions and troubleshooting exercises.
• Individual and group-based practical projects.
• Knowledge assessments and interactive technical evaluations.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.
 
b. Upon completion of training the participant will be issued with an Authorized Training Certificate
 
c. Course duration is flexible and the contents can be modified to fit any number of days.
 
d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.
 
e. One-year post-training support Consultation and Coaching provided after the course.
 f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.