Master the design and implementation of consistent streaming data pipelines using Apache Kafka, Spark, and Flink. In this hands-on course, you'll apply systematic decision frameworks to select appropriate delivery guarantees (at-most-once, at-least-once, exactly-once) based on business requirements and failure scenario analysis. You'll implement end-to-end exactly-once processing by configuring Kafka producer transactions, Spark Structured Streaming checkpoints, and Hudi transactional tables, then validate your implementation through integration testing with failure injection. Finally, you'll evaluate watermarking strategies by analyzing event arrival patterns to optimize the latency-completeness tradeoff and meet specific SLA requirements. Through realistic scenarios—from preventing duplicate billing in order processing to optimizing IoT event pipelines for sub-10-second P95 latency—you'll develop the skills to architect production streaming systems that balance correctness, performance, and operational simplicity.
Ensure Consistency in Streaming Pipelines
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Ensure Consistency in Streaming Pipelines
This course is part of Real-Time, Real Fast: Kafka & Spark for Data Engineers Specialization
Instructors: Starweaver
Included with
Recommended experience
What you'll learn
Stream pipeline design by analyzing failure scenarios and business requirements to prevent data loss or duplication.
Implement exactly-once processing semantics across producer, processor, and sink layers using transactions, checkpoints, and idempotent operations.
Evaluate watermarking and windowing configurations to optimize the tradeoff between latency and data completeness.
Skills you'll gain
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January 2026
1 assignment
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There are 3 modules in this course
Learn to select and justify appropriate delivery guarantees (at-most-once, at-least-once, exactly-once) for streaming pipelines by analyzing failure scenarios, business impact, and implementation costs. Apply a systematic decision framework that maps producer acknowledgments, consumer offset commits, and retry mechanisms to their resulting guarantees under failure conditions. Practice designing multi-tier pipelines where different segments require different guarantees based on use case requirements (monitoring, billing, compliance, analytics) and justify your selections during sprint planning and architecture reviews.
What's included
4 videos2 readings1 peer review
Implement end-to-end exactly-once processing by configuring coordinated mechanisms across Kafka producers (transactions and idempotence), Spark Structured Streaming (checkpoints and commit protocols), and Hudi transactional tables (primary keys and upsert semantics). Learn the specific configuration parameters required at each layer (transactional.id, checkpointLocation, recordkey.field) and understand how these mechanisms coordinate to prevent duplicates even under producer failures, consumer crashes, and checkpoint recovery scenarios. Validate your implementation through systematic integration testing with failure injection and SQL-based duplicate detection to prove production-grade consistency guarantees.
What's included
3 videos1 reading1 peer review
Learn to evaluate and tune watermarking strategies by analyzing empirical event arrival patterns from production systems to optimize the fundamental tradeoff between latency and data completeness. Analyze delay distributions (P50, P95, P99) to calculate achievable latency bounds, compare fixed-delay versus adaptive watermark strategies, and evaluate windowing configurations (tumbling, sliding, session) for their impact on memory footprint and result freshness. Apply evaluation criteria including measured end-to-end latency, late event drop rate, and computational resource usage to select watermark and window configurations that meet specific SLA requirements for IoT and real-time analytics use cases.
What's included
4 videos1 reading1 assignment2 peer reviews
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