DataOps Foundations: Build and Automate Modern Data Infrastructure

Behind every successful data pipeline lies a robust, automated infrastructure that keeps data flowing smoothly and securely. If you want to transition from simply writing data queries to building and maintaining the foundational systems that power data engineering, mastering DataOps is your next step. This text-based course guides you through the core principles of DataOps, teaching you how to configure environments, automate deployments, and orchestrate complex data workflows. You will transition from manual setups to building reliable, automated, and scalable data infrastructure using industry-standard tools. What you'll learn: Understand the foundational concepts of DataOps, Linux environment management, and version control with Git; Containerize data applications using Docker and orchestrate them within Kubernetes clusters; Automate repetitive infrastructure tasks and deployments using Ansible playbooks; Orchestrate and schedule complex data pipelines using Airflow; Configure CI/CD pipelines to automatically test and deploy your data engineering code; Apply modern observability and monitoring practices to track the health of your data workflows. You will start by exploring essential terminology, command-line operations, and version control basics before moving on to virtualization, containerization, and cluster management. Through clear written explanations, practical code configurations, and step-by-step infrastructure scenarios, you will build a complete understanding of how data tools operate under the hood. This course is designed for beginner data engineers, software developers, and systems administrators who want to specialize in data infrastructure. No prior DevOps experience is required, though a basic familiarity with Python is helpful. Start reading today to build the invisible foundation that keeps modern data systems running.