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.
Automate Data Pipelines: Schema Evolution
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您将学到什么
Build automated data pipelines with Apache Airflow, manage schema evolution to prevent failures, and implement monitoring for data integrity.
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该课程共有3个模块
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.
涵盖的内容
1个视频2篇阅读材料2个作业
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.
涵盖的内容
2个视频2篇阅读材料2个作业
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.
涵盖的内容
2个视频1篇阅读材料3个作业
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状态：免费试用
状态：免费试用
状态：免费试用
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