Build Real-Time Dashboards with Spark

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

• Explain Spark’s streaming model and produce a dashboard-ready table from a simple file source
• Construct a real-time pipeline that ingests from Kafka, processes with Spark, and stores result in Delta using event-time windows and watermarks
• Operate a production-oriented dashboard with refresh policies, monitoring, and failure recovery

• Basic familiarity with the topic and its common terminology
• Readiness to practice through applied exercises or case-based work