Orchestrate & Recover Real-Time Data Pipelines

Overview

Building a data pipeline is easy. Building one that automatically recovers from failures, maintains data integrity during outages, and runs reliably in production—that's what separates junior engineers from platform architects. This course teaches you to design self-healing pipelines with automated recovery, fault tolerance, and disaster recovery built in from day one. You'll learn to build and schedule streaming workflows using modern orchestrators like Airflow and Prefect, implement reliability patterns including idempotence, checkpointing, and dead-letter queues for exactly-once-ish processing, and design multi-region recovery strategies that keep data flowing during regional failures. Through hands-on labs and real-world examples from Airbnb, LinkedIn, Netflix, and Uber, you'll master the orchestration and recovery techniques that turn fragile scripts into production-grade infrastructure. Learn to handle automated retries, run safe backfills, implement checkpoint-based recovery, and execute disaster recovery playbooks that restore pipelines after outages. Engineers who build or maintain real-time data pipelines and need stronger orchestration, reliability, and recovery skills. Basics of Python & SQL, Linux CLI, and Kafka fundamentals. Cloud account helpful but optional. By the end of the course, learners will be able to design, orchestrate, and recover real-time data pipelines that run reliably at production scale.

Syllabus

Foundations of Orchestrating Real-Time Pipelines

Learners set up a modern orchestrator and build a first DAG/flow that runs reliably. We cover scheduling, retries, task dependencies, and lightweight observability. By the end, learners will ship a minimal but production-aware pipeline.

Reliability Patterns for Streaming: Idempotence, Checkpoints, and DLQs

We move from “works on my machine” to “recovers on its own.” Learners add exactly-once-ish processing, checkpointing, schema controls, and dead-letter queues. The module emphasizes designing for replay and safe backfills.

Recovery & DR: Backfills, Time Travel, and Cross-Region Replication

Learners design for failure domains—task, job, cluster, and region. We cover backfills vs. reprocessing, Delta time travel for safe fixes, and Kafka replication patterns (MirrorMaker 2, uReplicator) for DR.