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

This course is part of Open source Data Engineering with Spark, dbt & Airflow Professional Certificate

Instructor: Professionals from the Industry

Included with

Learn more

13 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

13 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

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.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

logos of Petrobras, TATA, Danone, Capgemini, P&G and L'Oreal

Build your Data Management expertise

This course is part of the Open source Data Engineering with Spark, dbt & Airflow Professional Certificate

When you enroll in this course, you'll also be enrolled in this Professional Certificate.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate from Coursera

There are 13 modules in this course

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.

Module details

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.

What's included

2 videos1 reading1 assignment

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.

What's included

3 videos1 reading2 assignments

3 videosTotal 17 minutes

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

1 readingTotal 10 minutes

Advanced Git History Analysis Techniques10 minutes

2 assignmentsTotal 18 minutes

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

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

What's included

2 videos1 reading2 assignments

2 videosTotal 11 minutes

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

1 readingTotal 12 minutes

Understanding Branching Models and Team Collaboration Patterns12 minutes

2 assignmentsTotal 20 minutes

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

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

What's included

3 videos1 reading2 assignments

3 videosTotal 14 minutes

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

1 readingTotal 10 minutes

GitHub Branch Protection Rules and Workflow Automation10 minutes

2 assignmentsTotal 21 minutes

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

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

What's included

3 videos1 reading1 assignment1 ungraded lab

3 videosTotal 15 minutes

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

1 readingTotal 10 minutes

Multi-stage Dockerfile Architecture for Data Processing10 minutes

1 assignmentTotal 3 minutes

Container Fundamentals Knowledge Check3 minutes

1 ungraded labTotal 60 minutes

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

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

What's included

2 videos2 readings2 assignments

2 videosTotal 13 minutes

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

2 readingsTotal 20 minutes

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

2 assignmentsTotal 15 minutes

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

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

What's included

2 videos1 reading2 assignments

2 videosTotal 9 minutes

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

1 readingTotal 8 minutes

Configuration Management Fundamentals for Data Infrastructure8 minutes

2 assignmentsTotal 21 minutes

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

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.

What's included

2 videos2 readings2 assignments1 ungraded lab

2 videosTotal 15 minutes

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

2 readingsTotal 20 minutes

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

2 assignmentsTotal 23 minutes

Ansible Automation Mastery Assessment15 minutes
Ansible Automation Implementation Knowledge Check8 minutes

1 ungraded labTotal 18 minutes

Create Ansible Playbooks for Automated Software Installation18 minutes

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

What's included

3 videos1 reading1 assignment

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

What's included

2 videos2 readings2 assignments1 ungraded lab

2 videosTotal 8 minutes

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

2 readingsTotal 18 minutes

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

2 assignmentsTotal 15 minutes

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

1 ungraded labTotal 20 minutes

Automated Data Pipeline Deployment with GitHub Actions20 minutes

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.

What's included

4 videos1 reading1 assignment

4 videosTotal 20 minutes

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

1 readingTotal 7 minutes

PostgreSQL Performance Monitoring Tools and Techniques7 minutes

1 assignmentTotal 3 minutes

PostgreSQL Performance Analysis Knowledge Check3 minutes

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

What's included

2 videos1 reading2 assignments

2 videosTotal 14 minutes

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

1 readingTotal 7 minutes

Strategic Database Resource Allocation for Performance Optimization7 minutes

2 assignmentsTotal 18 minutes

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

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.