Data Quality and Debugging for Reliable Pipelines

本课程是 Open source Data Engineering with Spark, dbt & Airflow 专业证书的一部分

位教师：Professionals from the Industry

访问权限由 New York State Department of Labor 提供

8个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

7 小时完成

灵活的计划

自行安排学习进度

8个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

7 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Define and automate data quality tests using YAML to validate row counts, null thresholds, and uniqueness across pipeline datasets.
Trace data anomalies through pipeline stages by analyzing logs and dashboards to identify and fix the exact source of failure.
Apply advanced Python debugging tools — including conditional breakpoints, watchpoints, and pdb — to diagnose and resolve pipeline issues.
Resolve complex concurrency bugs by reading stack traces and correlating thread logs to identify deadlocks and race conditions in code.

您将获得的技能

您将学习的工具

要了解的详细信息

可分享的证书

添加到您的领英档案

作业

12 任务¹

AI 评分请参见免责声明

授课语言：英语（English）

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累 Data Analysis 领域的专业知识

本课程是 Open source Data Engineering with Spark, dbt & Airflow 专业证书专项课程的一部分

在注册此课程时，您还会同时注册此专业证书。

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获得对主题或工具的基础理解
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通过 Coursera 获得可共享的职业证书

该课程共有8个模块

You'll build the diagnostic and preventive skills that keep data pipelines trustworthy and production-ready. In this course, you'll learn to define automated data quality tests, trace anomalies back to their source, and apply advanced Python debugging techniques to resolve complex pipeline failures — three capabilities that employers consistently seek in data engineering roles.

What sets this course apart is its end-to-end, practical focus: you won't just learn what data quality means — you'll write YAML test suites, navigate monitoring dashboards, analyze stack traces, and step through live code with debugging tools. Each skill builds toward a complete picture of pipeline reliability, from prevention to detection to resolution. By the end, you'll be equipped to catch data issues before they reach downstream consumers, communicate root causes clearly, and ship more dependable data products.

You will establish foundational understanding of data quality frameworks and define systematic approaches to testing data integrity through volume, completeness, and uniqueness validation.

涵盖的内容

3个视频1篇阅读材料1个作业

3个视频总计15分钟

Why Data Quality Frameworks Prevent Million-Dollar Pipeline Failures 2分钟
Essential Components of Data Quality Frameworks 7分钟
Implementing Basic Data Quality Tests with SQL 6分钟

1篇阅读材料总计8分钟

Data Quality Testing Patterns and Implementation Strategies 8分钟

1个作业总计3分钟

Data Quality Framework Foundation Knowledge Check 3分钟

You will implement automated data quality testing using YAML configuration and industry-standard tools to create production-ready validation systems with quality gates and monitoring capabilities.

涵盖的内容

2个视频3篇阅读材料2个作业1个非评分实验室

2个视频总计12分钟

How Automated Testing Saves Data Engineers from Midnight Crisis Calls 4分钟
Production-Ready Testing with dbt and Great Expectations 9分钟

3篇阅读材料总计25分钟

YAML-Based Testing Configuration and Great Expectations Integration 7分钟
Building YAML Test Suites for Production Validation 8分钟
Automated Data Pipeline Deployment 10分钟

2个作业总计18分钟

Automated Testing Implementation Mastery Check 3分钟
Data Quality Framework Mastery Assessment 15分钟

1个非评分实验室总计18分钟

Automated Data Pipeline Deployment with GitHub Actions 18分钟

You will learn systematic root cause analysis methodology for data pipeline anomalies through monitoring dashboard analysis and methodical investigation techniques.

涵盖的内容

1个视频2篇阅读材料1个作业1个非评分实验室

1个视频总计8分钟

Data Quality Investigation Framework: From Monitoring to Root Cause 8分钟

2篇阅读材料总计18分钟

Monitoring Dashboard Analysis: Reading the Signs of Pipeline Distress 10分钟
Navigating Monitoring Dashboards to Identify Data Anomaly Patterns 8分钟

1个作业总计3分钟

Data Quality Investigation Fundamentals Assessment 3分钟

1个非评分实验室总计18分钟

Systematic Data Pipeline Anomaly Investigation 18分钟

You will implement effective resolution strategies for pipeline integrity through targeted fixes, validation techniques, and systematic restoration procedures.

涵盖的内容

2个视频2篇阅读材料2个作业

2个视频总计16分钟

When Pipeline Fixes Become Production Heroes 5分钟
Pipeline Anomaly Resolution: A Structured Approach 11分钟

2篇阅读材料总计18分钟

Targeted Fix Implementation: SQL Solutions and Pipeline Restoration 10分钟
Implementing SQL Fixes and Validating Pipeline Restoration 8分钟

2个作业总计16分钟

Pipeline Resolution Strategy Validation 3分钟
Comprehensive Data Pipeline Troubleshooting Assessment 13分钟

You will learn systematic debugging approaches using conditional breakpoints, memory inspection, and methodical analysis techniques to transform from trial-and-error debugging to efficient problem resolution in Python data pipelines.

涵盖的内容

3个视频1篇阅读材料2个作业

3个视频总计14分钟

When Production Pipelines Fail: The Cost of Poor Debugging 3分钟
Advanced Debugging Fundamentals for Python Pipelines 6分钟
Setting Up Conditional Breakpoints in Production Code 5分钟

1篇阅读材料总计10分钟

Conditional Breakpoints and Memory Inspection Techniques 10分钟

2个作业总计18分钟

Hands-on Conditional Debugging in Multi-Batch Pipeline 15分钟
Advanced Debugging Techniques Knowledge Check 3分钟

You will develop systematic approaches to interpret complex stack traces, correlate log patterns, and reconstruct failure scenarios in multithreaded Python environments to identify concurrency issues like deadlocks and race conditions.

涵盖的内容

3个视频1篇阅读材料2个作业1个非评分实验室

3个视频总计17分钟

The Hidden Complexity of Multithreaded Debugging 4分钟
Understanding Stack Traces in Multithreaded Environments 6分钟
Analyzing ThreadPoolExecutor Stack Traces for Deadlock Detection 7分钟

1篇阅读材料总计10分钟

Log Correlation Techniques for Multithreaded Systems 10分钟

2个作业总计13分钟

Multithreaded Debugging Analysis Knowledge Check 3分钟
Production Multithreaded Debugging Mastery Assessment 10分钟

1个非评分实验室总计20分钟

Stack Trace Detective: Debugging Multithreaded Pipeline Failures 20分钟

You will create a comprehensive data quality monitoring system by building automated tests, investigating data anomalies, and debugging complex pipeline issues. This project integrates data quality frameworks, root cause analysis techniques, and advanced debugging skills into a single, production-ready solution.

涵盖的内容

4篇阅读材料1个作业

You will explore how generative AI tools enhance data engineering workflows across DevOps practices, performance optimization, and quality assurance. You will discover practical applications of AI assistance in version control, containerization, CI/CD automation, query tuning, and debugging.