Design Real-Time Architectures with Spark & Kafka

Overview

“Design Real-Time Architectures with Apache Spark & Kafka” is an intermediate-level course crafted for learners aiming to build modern, scalable streaming systems. Across engaging, scenario-driven lessons, the course offers a comprehensive introduction to designing and implementing real-time data pipelines. Participants explore the foundations of streaming concepts, event-driven patterns, and the unique demands of low-latency processing. They gain practical experience working with Apache Kafka for event ingestion and Apache Spark Structured Streaming for real-time computation, learning to transform raw streams into actionable insights. The curriculum emphasizes reliable pipeline design, covering fault tolerance, checkpointing, and performance tuning to ensure systems can operate at scale. Through hands-on practice, guided dialogues, and real-world financial data scenarios, learners develop the confidence to architect, optimize, and deploy production-ready streaming solutions. By the end of the course, they are equipped with the technical and strategic skills needed to excel in today’s data-driven, real-time environments. Learners should know basic Python or Scala, be comfortable with the command line, understand distributed systems at a high level, and have a simple introductory familiarity with Kafka and Spark. This course is ideal for aspiring data engineers, analysts or data scientists shifting into real-time systems, and software engineers exploring event-driven architecture. It also suits anyone working with large-scale data or financial and AI/ML pipelines who wants to understand how real-time data powers modern systems. By the end of the course, they are equipped with the technical and strategic skills needed to excel in today’s data-driven, real-time environments.

Syllabus

Fundamentals of Real-Time Architecture

This module introduces the core principles behind real-time data systems and how they differ from traditional batch processing. Learners explore key patterns such as event-driven design, streaming workflows, and the roles Kafka and Spark play in a modern data ecosystem. By the end, learners understand the foundational components required to build low-latency, scalable streaming architectures.

Building Real-Time Pipelines with Kafka & Spark

In this module, learners dive into the practical construction of streaming pipelines using Kafka and Spark Structured Streaming. They design Kafka topics, configure producers and consumers, and connect Spark to process incoming data streams. The module emphasizes transformations, windowing, and stateful operations essential for building functional real-world pipelines.

Productionizing & Optimizing Real-Time Architectures

This module focuses on preparing real-time systems for production environments. Learners explore fault tolerance, scalability strategies, and performance tuning for Kafka and Spark. They also learn how to monitor streaming workloads, implement checkpoints, and ensure reliability. The module concludes with best practices for deploying and maintaining robust, enterprise-ready real-time architectures.