DataOps Engineering with Python and Infrastructure Automation

Modern data organizations require a seamless bridge between software development and system operations. This text-based course guides you through the essentials of both Python development and DataOps infrastructure. You will transition from writing your first Python scripts to orchestrating complex data pipelines and managing containerized environments. By practicing with real-world scenarios, you will build the confidence to automate workflows, manage data platforms, and deploy scalable pipelines. What you'll learn: Write clean, modern Python code using type hints and structured packages; Manage system environments and version control using Linux commands and Git; Containerize applications and deploy scalable clusters with Docker and Kubernetes; Orchestrate complex data workflows and automate tasks using Airflow and Ansible; Process large-scale datasets by understanding Hadoop and Spark architectures; Implement modern CI/CD practices to automate testing and deployment of data pipelines. The course begins with foundational concepts in Python programming and command-line basics before advancing to containerization, orchestration, and big data tools. You will work through written explanations, structured code examples, and practical configurations. Designed for beginners with no prior programming or operations experience who want to enter the field of data engineering and DataOps. Start reading today to build your foundation in modern DataOps engineering.