Automate Data Pipelines: Schema Evolution

Overview

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.

Syllabus

Airflow Pipeline Development

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.

Schema Evolution Management

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.

Pipeline Monitoring and Reliability

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.