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Data Pipeline Automation & Orchestration: A Hands-On Guide to Building Your First Automated Workflows with Apache Airflow
Paperback
Series: Cloud Data Engineering Series: Pipelines, Automation, and Modern Architectures
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Publisher: Independently Published
Published: Sep 22 2025
Pages: 144
Weight: 0.58
Height: 0.31 Width: 7.00 Depth: 10.00
Language: English
Data pipelines are the backbone of every modern data-driven organization, yet too many teams still struggle with brittle scripts, unreliable workflows, and endless firefighting. What if you could replace those fragile processes with automated, production-ready pipelines you can trust every single day?
Data Pipeline Automation & Orchestration: A Hands-On Guide to Building Your First Automated Workflows with Apache Airflow gives you the practical knowledge to do exactly that. Designed for data engineers, analysts, and developers, this book shows how to build, automate, and scale workflows using Apache Airflow-the industry's most popular orchestration platform. Whether you are setting up your first DAG or deploying pipelines in the cloud, you'll learn to move beyond manual tasks and create systems that run dependably at scale.
What sets this book apart is its structured, project-based approach. Starting with the foundations of workflow automation, you'll install and configure Airflow across environments, then progress to writing DAGs, managing dependencies, and integrating with external systems. You'll explore monitoring, logging, and alerts to keep workflows healthy, and scaling strategies with Docker, Kubernetes, and CI/CD pipelines to prepare your projects for production. Advanced chapters cover branching workflows, dynamic DAG generation, event-driven pipelines, and security best practices. The book concludes with real-world walkthroughs-building an ETL pipeline, automating machine learning model training, and orchestrating multi-cloud data pipelines-that tie everything together in actionable, hands-on examples.
Inside, you'll learn how to:
Build Directed Acyclic Graphs (DAGs) that define reliable workflows in Python.
Automate ETL processes from raw data ingestion to analytics-ready tables.
Integrate Airflow with APIs, databases, and cloud platforms like AWS, GCP, and Azure.
Monitor and debug workflow failures with Airflow's built-in logging and alerting system.
Scale workflows using executors, Kubernetes deployments, and CI/CD pipelines.
Apply advanced orchestration patterns for conditional, dynamic, and event-driven pipelines.
Whether you're new to workflow automation or seeking to level up your orchestration skills, this book equips you with the hands-on expertise to transform the way you manage data pipelines.
Take the step from fragile scripts to resilient workflows. Start building automated, scalable pipelines with Apache Airflow today.
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