Real-Time, Real Fast: Kafka & Spark for Data Engineers

Modern organizations can’t wait until tomorrow to know what happened today: they need live visibility into orders per minute, anomaly rates, user activity, and so on. Real-time dashboards are no longer “nice to have”; they are essential for decision-making in e-commerce, finance, IoT, and operations. This course teaches you how to design and implement real-time dashboards powered by Apache Spark Structured Streaming. Through three hands-on modules, you’ll first master the streaming fundamentals for dashboarding: micro-batches, triggers, checkpoints, and schema enforcement. Next, you’ll integrate Spark with Kafka to process real-world event streams, apply event-time windows and watermarks to handle late or out-of-order data, and persist metrics into Delta Lake for reliable BI consumption. Finally, you’ll learn how to publish dashboards, configure refresh strategies, optimize performance with caches and materialized views, monitor pipeline health, and ensure recovery under failure. This course is ideal for data professionals, analysts, and engineers who want to build or operate real-time analytics systems. Whether you work in business intelligence, data engineering, or analytics, this course will help you turn streaming data into live, actionable dashboards. Learners should know basic Python and Spark DataFrames, and be familiar with SQL and JSON to follow the course smoothly. By the end, you won’t just know how to build a working dashboard; you’ll be able to operate one in production, keeping it accurate, fast, and trustworthy as data changes second by second.

Master Apache Kafka configuration, monitoring, and optimization for production environments. This hands-on course teaches you to design high-availability topic architectures, diagnose performance bottlenecks using consumer lag analysis, and tune producers and consumers for maximum throughput while meeting strict latency SLAs. Through real-world scenarios based on challenges faced by companies like Netflix, LinkedIn, Uber, and Walmart, you'll learn to prevent data loss during broker failures, eliminate consumer lag issues, and optimize Kafka clusters processing millions of events per second. By the end of this course, you'll have the skills to build, monitor, and optimize production Kafka infrastructure that handles massive scale while maintaining reliability and performance. This course is designed for software engineers, data platform specialists, and DevOps professionals who work with real-time data systems and want to deepen their expertise in Apache Kafka. Ideal learners already understand basic Kafka concepts and distributed systems fundamentals but seek to enhance their ability to configure, monitor, and optimize Kafka clusters for high-throughput, low-latency production environments. It’s also valuable for those preparing for roles in data engineering, site reliability, or systems performance optimization. Learners should have a basic understanding of distributed systems and networking concepts, familiarity with command-line interfaces, and introductory knowledge of Apache Kafka fundamentals such as topics, producers, and consumers. Prior experience with Linux environments, Docker, or monitoring tools like Grafana and Prometheus will be helpful but not required. By the end of this course, you’ll be able to configure and optimize Apache Kafka clusters for high throughput, low latency, and maximum availability. You’ll gain hands-on experience in monitoring broker health, diagnosing consumer lag, and tuning producer and consumer performance for real-world production environments. With these skills, you’ll be ready to build, scale, and maintain data streaming systems that power modern, high-performance applications.

Real-time data is everywhere — from fraud detection in financial transactions to personalized recommendations in e-commerce and anomaly detection in IoT devices. Traditional batch processing is too slow for these use cases, and businesses need insights the moment data is generated. This course teaches you how to design, build, and operate reliable streaming pipelines using Apache Spark Structured Streaming and Kafka. In this course, you’ll start with the fundamentals of Spark’s streaming model, learning how micro-batching, triggers, and checkpoints enable continuous processing. You’ll then connect Spark to real-world sources like Kafka, apply event-time processing with watermarks, and deliver results to Delta Lake. Finally, you’ll take pipelines to production by enriching streams with static data, monitoring query health, handling failures, and ensuring scalability. This course introduces you to real-time data processing using Apache Spark Streaming. You’ll learn how to handle continuous data flows, design fault-tolerant stream pipelines, and analyze live data efficiently. By the end, you’ll understand how Spark handles streaming workloads, integrates with various data sources, and powers decision-making in real-world applications. Learners should have a basic understanding of Python programming and Spark DataFrames, along with familiarity with JSON and SQL. By the end, you’ll have the skills to confidently implement streaming solutions that power real-time decision-making in modern data-driven organizations.