Overview

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. Intermediate data and platform engineers using Kafka, Spark, or Flink who want to design production streaming pipelines with correct delivery guarantees, exactly-once semantics, and low-latency processing. Foundational knowledge of distributed systems; basic experience with Apache Kafka or similar messaging systems; familiarity with SQL; and introductory experience with stream or batch data processing concepts. By the end of this course, you will be able to design and validate production-ready streaming pipelines with correct delivery guarantees, exactly-once semantics, and low-latency event-time processing.

Syllabus

Apply Delivery Guarantees to Pipeline Design

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.

Implement Exactly-Once Processing Semantics

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.

Evaluate Watermarking Strategies for Latency-Completeness Tradeoffs

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.