“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.
Design Real-Time Architectures with Spark & Kafka
Design Real-Time Architectures with Spark & Kafka
This course is part of Real-Time, Real Fast: Kafka & Spark for Data Engineers Specialization
Instructors: Soheil Haddadi
Included with
Recommended experience
What you'll learn
Examine core real-time data principles and how Kafka and Spark support streaming architectures.
Create real-time pipelines by connecting Kafka topics with Spark Structured Streaming.
Improve and deploy streaming systems using monitoring, fault tolerance, and tuning.
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February 2026
1 assignment
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There are 3 modules in this course
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.
What's included
4 videos2 readings1 peer review
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.
What's included
3 videos1 reading1 peer review
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.
What's included
4 videos1 reading1 assignment2 peer reviews
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