“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
位教师：Soheil Haddadi
访问权限由 New York State Department of Labor 提供
您将学到什么
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.
您将获得的技能
- Performance Management
- Event-Driven Programming
- Data Transformation
- System Monitoring
- Real-Time Operating Systems
- Application Deployment
- Software Architecture
- Distributed Computing
- Systems Architecture
- Performance Tuning
- Scalability
- Data Processing
- Apache Spark
- Architecture and Construction
- Real Time Data
- Data Pipelines
- Apache Kafka
- 技能部分已折叠。显示 8 项技能，共 17 项。
要了解的详细信息
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1 项作业
February 2026
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该课程共有3个模块
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.
涵盖的内容
4个视频2篇阅读材料1次同伴评审
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.
涵盖的内容
3个视频1篇阅读材料1次同伴评审
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.
涵盖的内容
4个视频1篇阅读材料1个作业2次同伴评审
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