Apache Airflow Best Practices: A practical guide to orchestrating data workflow with Apache Airflow by Dylan Intorf, Dylan Storey, Kendrick van Doorn
English | October 31, 2024 | ISBN: 1805123750 | 188 pages | EPUB | 4.22 Mb
Confidently orchestrate your data pipelines with Apache Airflow by applying industry best practices and scalable strategies
Key FeaturesSeamlessly migrate from Airflow 1.x to 2.x and explore the key features and improvements in version 2.xLearn Apache Airflow workflow authoring through practical, real-world use casesDiscover strategies to optimize and scale Airflow pipelines for high availability and operational resiliencePurchase of the print or Kindle book includes a free PDF eBookBook Description
Data professionals face the challenge of managing complex data pipelines, orchestrating workflows across diverse systems, and ensuring scalable, reliable data processing. This definitive guide to mastering Apache Airflow, written by experts in engineering, data strategy, and problem-solving across tech, financial, and life sciences industries, is your key to overcoming these challenges.
Covering everything from Airflow fundamentals to advanced topics such as custom plugin development, multi-tenancy, and cloud deployment, this book provides a structured approach to workflow orchestration. You'll start with an introduction to data orchestration and Apache Airflow 2.x updates, followed by DAG authoring, managing Airflow components, and connecting to external data sources. Through real-world use cases, you'll learn how to implement ETL pipelines and orchestrate ML workflows in your environment, and scale Airflow for high availability and performance. You'll also learn how to deploy Airflow in cloud environments, tackle operational considerations for scaling, and apply best practices for CI/CD and monitoring.
By the end of this book, you'll be proficient in operating and using Apache Airflow, authoring high-quality workflows in Python, and making informed decisions crucial for production-ready Airflow implementations.
What you will learnExplore the new features and improvements in Apache Airflow 2.0Design and build scalable data pipelines using DAGsImplement ETL pipelines, ML workflows, and advanced orchestration strategiesDevelop and deploy custom plugins and UI extensionsDeploy and manage Apache Airflow in cloud environments such as AWS, GCP, and AzurePlan and execute a scalable deployment strategy for long-term growthApply best practices for monitoring and maintaining AirflowWho this book is for
This book is ideal for data engineers, developers, IT professionals, and data scientists looking to optimize workflow orchestration with Apache Airflow. It's perfect for those who recognize Airflow's potential and want to avoid common implementation pitfalls. Whether you're new to data, an experienced professional, or a manager seeking insights, this guide will support you. A functional understanding of Python, some business experience, and basic DevOps skills are helpful. While prior experience with Airflow is not required, it is beneficial.
Table of ContentsGetting Started with Airflow 2.0Core Airflow ConceptsComponents of AirflowBasics of Airflow and DAG AuthoringConnecting to External SourcesExtending Functionality with UI PluginsWriting and Distributing Custom ProvidersOrchestrating a Machine Learning WorkflowUsing Airflow as a Driving ServiceAirflow Ops: Development and DeploymentAirflow Ops Best Practices: Observation and MonitoringMulti-Tenancy in AirflowMigrating Airflow
AusFile
Fileaxa
Links are Interchangeable - Single Extraction
erivatives & Gradients - the backbone of optimization algorithmsIntegrals & Area Under the Curve - from probability to AUC-ROC curvesMultivariable Calculus - powering neural networks, backpropagation, and moreHands-on examples with Python - bringing theory to life with codeUse cases in machine learning, statistics, and deep learningDesigned for accessibility without sacrificing depth, this book is ideal for students, self-taught developers, analysts, and anyone preparing for a career in AI, data science, or fintech.
