Overview

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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.

Syllabus

Apply Merge Conflict Resolution Techniques

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.

Analyze Commit History for Bug Tracing

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.

Branching Strategy Fundamentals

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

Implementation & Process Design

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

Container Fundamentals & Multi-stage Dockerfiles

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

Image Versioning & Registry Publishing

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

Configuration Management Foundations

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

Ansible Automation Implementation

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.

CI/CD Pipeline Fundamentals

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

Automated Data Deployment

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

Query Performance Analysis Foundations

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.

Resource Allocation and Optimization

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

Project: DevOps and CI/CD for Data Engineering Performance

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.