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⇱ Automate Data Pipelines: Schema Evolution | Coursera

Automate Data Pipelines: Schema Evolution

Automate Data Pipelines: Schema Evolution

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Intermediate level

Recommended experience

2 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

2 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Build automated data pipelines with Apache Airflow, manage schema evolution to prevent failures, and implement monitoring for data integrity.

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Recently updated!

December 2025

Assessments

7 assignments¹

AI Graded see disclaimer
Taught in English

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This course is part of the LLM Optimization & Evaluation Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 3 modules in this course

Automate Data Pipelines: Schema Evolution is an intermediate course designed for data engineers, analysts, and developers looking to build robust, failure-resistant data workflows. In today's dynamic data landscape, pipelines often break when source data structures change unexpectedly—a problem known as schema drift. This course tackles that challenge head-on, teaching you how to design and automate data pipelines that can gracefully handle schema evolution using Apache Airflow.

You will gain hands-on experience designing, building, and scheduling complex data pipelines (DAGs) that automate ETL processes from extraction to loading. The curriculum places a strong emphasis on creating idempotent workflows that detect and adapt to schema changes, ensuring data integrity and preventing costly failures. Through practical labs and real-world case studies from companies like Uber and BharatPe, you will implement data validation checks and build comprehensive monitoring and alerting systems. By the end of this course, you will be equipped to create resilient, scalable, and fully automated data pipelines that are built to withstand the complexities of real-world data environments.

This module provides a deep dive into the world of workflow automation with Apache Airflow. You will move from understanding the core concepts of DAGs and operators to building a complete, scheduled data pipeline in a hands-on lab. The focus is on creating robust, idempotent workflows that form the backbone of reliable data systems.

What's included

1 video2 readings2 assignments

1 video•Total 5 minutes
  • Coding and Scheduling Your First DAG•5 minutes
2 readings•Total 9 minutes
  • The Core Components of Airflow•5 minutes
  • How-To: Managing Connections and Variables•4 minutes
2 assignments•Total 15 minutes
  • Hands-On Learning: Automating an Article Processing Workflow•10 minutes
  • Knowledge Check: Airflow Fundamentals•5 minutes

Data sources are not static. This module addresses the critical skill of managing schema evolution. You will learn how to analyze the downstream impact of source data changes and use dbt to adapt your data quality tests, ensuring your pipelines remain robust and trustworthy even as data structures evolve.

What's included

2 videos2 readings2 assignments

2 videos•Total 10 minutes
  • The Silent Pipeline Killer: Schema Drift•4 minutes
  • Writing and Adapting dbt Tests•5 minutes
2 readings•Total 7 minutes
  • Understanding Schema Drift and Data Lineage•3 minutes
  • How-To: Documenting and Communicating Schema Changes•4 minutes
2 assignments•Total 17 minutes
  • Hands-On Learning: Handling Schema Evolution with dbt Testing•12 minutes
  • Knowledge Check: Schema Impact•5 minutes

This module extends beyond building pipelines to tackle "silent failures"—where a successful run produces bad data—and establishes observability as the core defense. You will instrument Airflow DAGs to emit key health metrics like freshness, volume, and duration, and configure automated alerts using on_failure_callback. By the end, you will construct resilient pipelines that fail loudly, ensuring data integrity and stakeholder trust.

What's included

2 videos1 reading3 assignments

2 videos•Total 9 minutes
  • When a Tree Falls: The Danger of Silent Failures•3 minutes
  • Building-In Failure Alerts•6 minutes
1 reading•Total 5 minutes
  • Designing for Observability•5 minutes
3 assignments•Total 50 minutes
  • Final Project: Building a Resilient & Monitored Pipeline•30 minutes
  • Hands-On Learning: Enhancing Your DAG with Monitoring and Alerting•15 minutes
  • Knowledge Check: Monitoring Concepts•5 minutes

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¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.