What is a DevOps workflow for data engineering in this course?

In this course, a DevOps workflow for data engineering means using a repeatable process to manage code changes, package environments, automate setup, and move pipeline changes safely across environments. The focus is on connecting version control, containerization, automation, CI/CD, and performance work into one practical way of operating data systems.

When would you use this kind of DevOps workflow?

You would use it when data pipeline changes need to be made consistently by individuals or teams without relying on ad hoc fixes. It becomes especially useful when merge conflicts, environment drift, manual server setup, or risky deployments start slowing down everyday work.

How does this DevOps workflow fit into a broader data engineering process?

It sits between writing or updating pipeline logic and keeping that work reliable in development, staging, and production. In this course, the workflow turns separate tasks like coding, setup, deployment, and performance checks into a connected process you can repeat.

How is this DevOps workflow different from handling data pipeline changes with separate manual steps?

A DevOps workflow is built to make collaboration, setup, deployment, and validation repeatable instead of depending on one-off decisions or manual coordination. Here, that difference shows up through structured branching, automated configuration, containerized environments, and CI/CD promotion between environments.

Do you need any prerequisites before learning this DevOps workflow?

Because the course is beginner level, you do not need deep DevOps experience before starting. A basic comfort with code files, version control concepts, and working through technical steps is helpful since the course centers on applying a connected workflow rather than only discussing ideas.

What tools, platforms, or methods are used in this course?

The course centers on Git, Docker, and Ansible, then ties them together with CI/CD automation and query performance analysis. The emphasis is on using those tools as parts of one workflow, not studying each one in isolation.

What specific tasks will you practice or complete in this course?

You practice resolving merge conflicts, designing branching strategies, containerizing data environments, automating server configuration, and promoting data pipeline artifacts through CI/CD stages. You also trace bugs through Git history and analyze query behavior so the overall workflow supports stable, production-focused data systems.

DevOps and CI/CD for Data Engineering Performance

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

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13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

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

13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

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

您将学到什么

Resolve merge conflicts and trace bugs using Git history tools, keeping collaborative codebases stable and production-ready.
Design branching strategies and automate deployments with CI/CD pipelines to safely promote data pipeline artifacts across environments.
Build and publish versioned Docker images and automate server configuration with Ansible for consistent, reproducible environments.
Analyze query execution metrics and optimize resource allocation to maintain performance targets in production data systems.

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

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该课程共有13个模块

You'll build the skills to manage, automate, and optimize production-grade data systems using industry-standard DevOps practices. By completing this course, you'll be able to resolve complex version control conflicts, design branching strategies for collaborative development, containerize data environments with Docker, automate infrastructure configuration with Ansible, deploy data pipelines through CI/CD workflows, and optimize query performance to maintain service levels.

This course is unique because it bridges the gap between software engineering and data engineering — giving you hands-on experience with the exact tools and workflows used in real production environments. Rather than covering concepts in isolation, you'll integrate version control, containerization, automation, and performance tuning into a cohesive DevOps skillset that employers actively seek. Whether you're moving into a data engineering role or strengthening your current practice, you'll finish with portfolio-ready work that demonstrates job-ready capability.

单元详情

You will learn systematic approaches to resolve merge conflicts that automated Git processes cannot handle, distinguishing between text-based line conflicts and binary file selection strategies in data engineering environments.

涵盖的内容

2个视频1篇阅读材料1个作业

You will learn systematic debugging techniques using Git's historical analysis capabilities to identify the exact commit that introduced software defects through binary search and commit analysis methodologies.

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17分钟

Why Git Forensics Transforms Debugging from Guesswork to Science4分钟
Git Bisect: Binary Search Algorithm for Bug Detection9分钟
Automated Git Bisect with Custom Test Scripts4分钟

1篇阅读材料总计10分钟

Advanced Git History Analysis Techniques10分钟

2个作业总计18分钟

SQL Schema Merge Conflict Resolution 15分钟
Bug Tracing and Git History Analysis Knowledge Check3分钟

You will understand fundamental branching models and design strategic workflows that enable parallel development while maintaining code stability.

涵盖的内容

2个视频1篇阅读材料2个作业

2个视频总计11分钟

Why Version Control Strategy Matters in Data Engineering Teams4分钟
Branch Naming Conventions and Merge Protocol Design8分钟

1篇阅读材料总计12分钟

Understanding Branching Models and Team Collaboration Patterns12分钟

2个作业总计20分钟

Design Your Team's Branching Workflow Documentation13分钟
Branching Strategy Fundamentals Knowledge Check7分钟

You will implement their branching strategy using GitHub's protection features and automation tools, creating a production-ready development.

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

Scaling Development Teams Through Strategic Implementation4分钟
Configuring GitHub Branch Protection and Required Reviews5分钟
Setting Up Automated GitHub Actions for Branch Workflows5分钟

1篇阅读材料总计10分钟

GitHub Branch Protection Rules and Workflow Automation10分钟

2个作业总计21分钟

Complete Branching Strategy Implementation Project15分钟
GitHub Implementation and Workflow Automation Knowledge Check6分钟

You will learn containerization fundamentals and create production-ready multi-stage Dockerfiles for data processing environments.

涵盖的内容

3个视频1篇阅读材料1个作业1个非评分实验室

3个视频总计15分钟

Why Containerization Transforms Data Engineering Workflows3分钟
Container Fundamentals for Data Processing Environments9分钟
Building Multi-stage Dockerfiles for Spark Data Processing3分钟

1篇阅读材料总计10分钟

Multi-stage Dockerfile Architecture for Data Processing10分钟

1个作业总计3分钟

Container Fundamentals Knowledge Check3分钟

1个非评分实验室总计60分钟

Build Production-Ready Multi-stage Dockerfile for Data Processing60分钟

You will implement systematic version tagging strategies and integrate with enterprise container registries for automated deployment workflows.

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13分钟

Systematic Container Image Tagging for Data Infrastructure8分钟
Setting Up Amazon ECR Repository and Authentication5分钟

2篇阅读材料总计20分钟

Enterprise Container Registry Integration Value Proposition10分钟
Amazon ECR Integration Patterns for Data Engineering Teams10分钟

2个作业总计15分钟

Complete Containerization Workflow Mastery Assessment12分钟
Container Registry Integration and Deployment Workflow Concepts3分钟

You will understand why automation tools are essential for scalable infrastructure management and explore foundational configuration management concepts through real-world enterprise scenarios.

涵盖的内容

2个视频1篇阅读材料2个作业

2个视频总计9分钟

The Infrastructure Challenge: From Manual Chaos to Automated Excellence2分钟
Ansible Architecture and Automation Workflow6分钟

1篇阅读材料总计8分钟

Configuration Management Fundamentals for Data Infrastructure8分钟

2个作业总计21分钟

Design Your First Configuration Management Strategy18分钟
Ansible Fundamentals Knowledge Check 3分钟

You will create functional Ansible playbooks that automate Python installation, pip package management, systemd service configuration, and webserver verification to achieve consistent server deployments across multiple environments.

涵盖的内容

2个视频2篇阅读材料2个作业1个非评分实验室

2个视频总计15分钟

Advanced Playbook Features: Variables, Templates, and Error Handling6分钟
Building a Complete Python Web Server Deployment9分钟

2篇阅读材料总计20分钟

Enterprise Automation Success Stories: From Manual Chaos to Scalable Infrastructure8分钟
Understanding Ansible Playbooks: Components and Structure12分钟

2个作业总计23分钟

Ansible Automation Mastery Assessment15分钟
Ansible Automation Implementation Knowledge Check8分钟

1个非评分实验室总计18分钟

Create Ansible Playbooks for Automated Software Installation18分钟

You will learn the foundational concepts and practical applications of CI/CD pipelines for data deployment automation.

涵盖的内容

3个视频1篇阅读材料1个作业

You will implement comprehensive automated deployment workflows that safely promote data pipeline components from staging to production with proper validation and monitoring.

涵盖的内容

2个视频2篇阅读材料2个作业1个非评分实验室

2个视频总计8分钟

Advanced GitHub Actions for Production Deployments5分钟
Building Complete GitHub Actions Deployment Pipeline3分钟

2篇阅读材料总计18分钟

Enterprise Data Deployment Challenges and Automation Solutions8分钟
Monitoring and Validation Strategies for Automated Deployments10分钟

2个作业总计15分钟

Comprehensive CI/CD Pipeline Implementation Assessment10分钟
Advanced Data Deployment Automation Knowledge Check5分钟

1个非评分实验室总计20分钟

Automated Data Pipeline Deployment with GitHub Actions20分钟

You will learn the fundamentals of query performance analysis by learning to identify bottlenecks, interpret execution plans, and understand key performance metrics that guide optimization decisions.

涵盖的内容

4个视频1篇阅读材料1个作业

4个视频总计20分钟

Why Query Performance Analysis Prevents System Failures3分钟
Query Performance Fundamentals for Data Engineers6分钟
Interpreting Query Execution Plans for Optimization6分钟
Using pg_stat_activity to Identify Performance Issues6分钟

1篇阅读材料总计7分钟

PostgreSQL Performance Monitoring Tools and Techniques7分钟

1个作业总计3分钟

PostgreSQL Performance Analysis Knowledge Check3分钟

You will apply performance analysis insights to make strategic resource allocation decisions and implement targeted optimizations that maintain service level agreements in production environments.

涵盖的内容

2个视频1篇阅读材料2个作业

2个视频总计14分钟

Strategic Resource Allocation for Service Level Agreements6分钟
Implementing Memory and Index Optimization in PostgreSQL8分钟

1篇阅读材料总计7分钟

Strategic Database Resource Allocation for Performance Optimization7分钟

2个作业总计18分钟

Query Performance Analysis and Resource Allocation Mastery15分钟
PostgreSQL Resource Allocation Knowledge Check3分钟

You will create a complete DevOps workflow that integrates version control, containerization, automation, and performance optimization to deploy and maintain data engineering systems. This project combines Git conflict resolution, Docker containerization, Ansible automation, CI/CD pipeline design, and query performance optimization into a realistic enterprise deployment scenario.