Hands-On Apache Beam with Kotlin: Practical Data Pipeline Exercises — PickAClass
⏱ 2h 42m 📚 27 lessons

Hands-On Apache Beam with Kotlin: Practical Data Pipeline Exercises

Build robust batch and streaming data pipelines by solving practical, step-by-step programming challenges using Kotlin and Apache Beam.

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About this course

Processing large-scale data efficiently requires both powerful frameworks and expressive programming languages. This text-based course guides you through the fundamentals of building data processing pipelines using Apache Beam and Kotlin, combining the unified programming model of Beam with the modern, concise syntax of Kotlin. You will transition from understanding core data pipeline concepts to writing, refactoring, and optimizing your own pipeline code through structured, written exercises. What you'll learn: • Understand the core concepts of Apache Beam, including Pipelines, PCollections, and transforms. • Implement batch and streaming data pipelines using Kotlin's expressive and type-safe syntax. • Apply windowing, triggering, and accumulation strategies to handle out-of-order data. • Write custom PTransforms to modularize and reuse your data processing logic. • Practice debugging and testing Apache Beam pipelines using local runners. • Integrate modern Kotlin features like null safety and data classes to write cleaner pipeline code. Starting with fundamental definitions and setup, you will progress through a series of written programming challenges designed to build your confidence step by step. This course is designed for software engineers, data engineers, and Kotlin developers who are new to Apache Beam and want a practical, text-based path to mastery. No prior experience with Apache Beam is required. Start reading today to build your data engineering skills with Kotlin and Apache Beam.

What you'll get

  • 📜 Certificate of completion
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  • Short & focused
    2h 42m of practical content

Certificate of completion

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Hands-On Apache Beam with Kotlin: Practical Data Pipeline Exercises
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Performance detail
Coursework summary
Lessons completed 14 / 14
Practice questions 26 / 28
Assignments submitted 4 (avg 4.5 / 5)
Capstone project Reviewed — 4.6 / 5
Total practice 6.2 hrs
Performance benchmark
Cohort rank Top 12% of 1,625
Time to completion 11 days (median: 22)
Mastery score 91 / 100
Practice-question score 94%
Skill verification Verified Skill Path
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Issued under the academic standards of PickAClass. Skill levels reflect assessed performance against the course's competency rubric. This is an original credential of this platform.

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