Building Event-Driven Data Streaming Architectures with Apache Kafka and Apache Spark

About the Course

This comprehensive training program equips data engineers and architects with the skills needed to design and implement scalable, fault-tolerant streaming architectures. Participants will gain deep expertise in Apache Kafka's distributed messaging capabilities and Apache Spark's unified processing framework, learning how to build systems that handle real-time data at scale with reliability and performance.

Through practical labs and real-world scenarios, this course bridges the gap between theory and production implementation, ensuring participants can immediately apply their knowledge to enterprise data pipelines.

Course Objectives

• Design and configure Kafka clusters for high-throughput, low-latency message distribution
• Implement Spark Streaming and Structured Streaming for real-time data processing
• Build end-to-end data pipelines integrating Kafka with Spark and other data systems
• Develop strategies for fault tolerance, exactly-once processing, and data consistency
• Monitor, optimize, and troubleshoot streaming applications in production environments
• Implement security best practices including authentication, encryption, and access control
• Design event-driven architectures and handle complex streaming workflows
• Integrate streaming pipelines with data lakes, data warehouses, and real-time analytics platforms

Target Audience

• Data engineers responsible for building and maintaining data pipelines
• Solutions architects designing real-time data systems
• Platform engineers implementing streaming infrastructure
• Technical leads overseeing data streaming initiatives
• DevOps professionals managing data streaming deployments

What You Will Benefit as a Learner

• Practical Expertise: Hands-on experience configuring and deploying Kafka clusters and Spark streaming applications in realistic environments
• Production-Ready Skills: Learn monitoring, debugging, and optimization techniques used in enterprise deployments
• Architecture Knowledge: Understand patterns for designing scalable, fault-tolerant streaming systems that meet business requirements
• Problem-Solving Capabilities: Develop solutions for common challenges like data quality, late arrivals, stateful processing, and exactly-once semantics
• Industry Best Practices: Gain knowledge of security, performance tuning, and operational excellence from experienced practitioners
• Certification-Ready Foundation: Prepare for industry certifications in data engineering and streaming technologies

Training Methodology

• Interactive Lectures: Expert-led sessions covering architecture, design patterns, and technical concepts with real-world case studies
• Hands-On Labs: Practical exercises using Docker-based environments where participants build and deploy actual Kafka and Spark applications
• Collaborative Projects: Small group activities solving real streaming challenges with peer learning and instructor guidance
• Code Reviews: Detailed analysis of production code samples and participant implementations with feedback
• Q&A Sessions: Regular open forums to discuss challenges, troubleshoot issues, and share experiences
• Resource Library: Access to templates, scripts, configuration files, and reference architectures for future use