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Day 2 — Stateful Processing, Output Modes & Fault Tolerance
4 Lessons
This course is not open for enrollment
RADE™ Spark Structured Streaming for Real-Time Data Engineering
Building Real-Time Streaming Data Pipelines with Spark
Course Summary
Most data engineers start their careers working with batch data — daily jobs, scheduled pipelines, and reports that arrive hours later.
But modern companies don’t work that way anymore.
Today’s systems power:
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Live dashboards
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Real-time alerts
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Fraud detection
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Streaming analytics
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Event-driven platforms
And all of this is built on streaming data.
This course is designed for data engineers who have never worked with streaming before and want a clear, structured, confidence-building entry into real-time data processing using Apache Spark.
You will learn:
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What streaming data really means (in simple terms)
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How real-time systems are different from batch pipelines
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How Spark processes data continuously, not just once a day
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How modern platforms handle late, out-of-order, and constantly arriving data
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How real-time pipelines are designed in the real world
Instead of starting with complex theory, this course builds understanding step by step, helping you develop the intuition required to work with streaming systems.
By the end of the course, you will:
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Clearly understand how real-time data flows through modern data platforms
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Be able to read, write, and reason about Spark streaming pipelines
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Confidently talk about streaming concepts in interviews and at work
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Be prepared to move into advanced streaming systems like Kafka-based architectures
This course is part of the RADE™ Applied Data Engineering Mastery Program and acts as a gateway skill — opening the door to high-impact, real-time data engineering roles.
You don’t need prior streaming experience.
You just need the desire to move beyond batch-only data engineering.
Course Curriculum
Sachin Chandrashekhar
