DataOps With Python & Airflow

DataOps With Python&Airflow Orchestrate Automated Workflows with Full Control

This book is intended for students and data, DevOps, and engineering professionals seeking to master DataOps with a focus on automation and continuous integration in modern environments. Combining data engineering practices and scalable pipelines, it covers the practical application of Python and Apache Airflow in workflow orchestration, dependency control, automated deployments, and integration with leading market tools.

You will learn how to structure reproducible environments, connect multiple data sources, apply versioning to pipelines, and operate securely in the cloud.

Includes:

- DAG orchestration with Apache Airflow and Python

- Workflow deployment with Git, Docker, and Kubernetes

- Integration with SQL, NoSQL databases, and Data Lakes

- Monitoring with Grafana, Prometheus, and smart alerts

- Pipeline versioning with GitLab CI/CD and dbt

- Scalable execution in AWS, Azure, Google Cloud, and hybrid environments

- Security, governance, and automation of critical tasks

Master DataOps and accelerate your performance in data engineering with continuous integration, full automation, and auditable pipelines.

airflow, python, pipelines, continuous integration, data engineering, ci/cd, kubernetes, grafana, dbt, cloud computing