DevOps Foundations for Data Engineers: Git, Docker, and CI/CD

Modern data engineering requires more than just writing SQL queries and scripts; it demands reliable, automated, and reproducible pipelines. Transitioning your data workflows from local scripts to production-grade systems requires a solid understanding of operational practices. This text-based course guides you through the essential tools and methodologies needed to bridge the gap between data engineering and DevOps. By completing this course, you will transition from manual, error-prone deployments to automated, containerized, and version-controlled data pipelines that run reliably in any environment. You will gain the confidence to collaborate with engineering teams and maintain high-quality data infrastructure. What you'll learn: - Understand foundational version control concepts and Git workflows to track changes and collaborate on data code. - Build and configure Docker containers to package data applications, ensuring environment consistency across systems. - Create automated CI/CD pipelines to test, validate, and deploy data workflows seamlessly. - Manage multi-container environments using Docker Compose to orchestrate local data stacks. - Apply modern configuration management and environment practices tailored for data pipelines. This course begins with core terminology and foundational concepts before advancing to practical deployment scenarios. Through clear written explanations and structured code snippets, you will analyze configurations and learn how to apply these patterns to your own projects. This course is designed for beginner data engineers, data analysts, and database administrators who want to adopt modern development practices. No prior experience with Docker or CI/CD is required. Start building more resilient, automated, and production-ready data workflows today.