Apache Airflow Best Practices
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Apache Airflow Best Practices
Confidently orchestrate your data pipelines with Apache Airflow by applying industry best practices and scalable strategies Key Features Seamlessly migrate from Airflow 1.x to 2.x and explore the key features and improvements in version 2.x Learn Apache Airflow workflow authoring through practical, real-world use cases Discover strategies to optimize and scale Airflow pipelines for high availability and operational resilience Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionData 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 learn Explore the new features and improvements in Apache Airflow 2.0 Design and build scalable data pipelines using DAGs Implement ETL pipelines, ML workflows, and advanced orchestration strategies Develop and deploy custom plugins and UI extensions Deploy and manage Apache Airflow in cloud environments such as AWS, GCP, and Azure Plan and execute a scalable deployment strategy for long-term growth Apply best practices for monitoring and maintaining Airflow Who 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.
Mastering Apache Airflow
Empower Your Data Workflow Orchestration and Automation Are you ready to embark on a journey into the world of data workflow orchestration and automation with Apache Airflow? "Mastering Apache Airflow" is your comprehensive guide to harnessing the full potential of this powerful platform for managing complex data pipelines. Whether you're a data engineer striving to optimize workflows or a business analyst aiming to streamline data processing, this book equips you with the knowledge and tools to master the art of Airflow-based workflow automation.
Data Engineering Best Practices
Author: Richard J. Schiller
language: en
Publisher: Packt Publishing Ltd
Release Date: 2024-10-11
Explore modern data engineering techniques and best practices to build scalable, efficient, and future-proof data processing systems across cloud platforms Key Features Architect and engineer optimized data solutions in the cloud with best practices for performance and cost-effectiveness Explore design patterns and use cases to balance roles, technology choices, and processes for a future-proof design Learn from experts to avoid common pitfalls in data engineering projects Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionRevolutionize your approach to data processing in the fast-paced business landscape with this essential guide to data engineering. Discover the power of scalable, efficient, and secure data solutions through expert guidance on data engineering principles and techniques. Written by two industry experts with over 60 years of combined experience, it offers deep insights into best practices, architecture, agile processes, and cloud-based pipelines. You’ll start by defining the challenges data engineers face and understand how this agile and future-proof comprehensive data solution architecture addresses them. As you explore the extensive toolkit, mastering the capabilities of various instruments, you’ll gain the knowledge needed for independent research. Covering everything you need, right from data engineering fundamentals, the guide uses real-world examples to illustrate potential solutions. It elevates your skills to architect scalable data systems, implement agile development processes, and design cloud-based data pipelines. The book further equips you with the knowledge to harness serverless computing and microservices to build resilient data applications. By the end, you'll be armed with the expertise to design and deliver high-performance data engineering solutions that are not only robust, efficient, and secure but also future-ready.What you will learn Architect scalable data solutions within a well-architected framework Implement agile software development processes tailored to your organization's needs Design cloud-based data pipelines for analytics, machine learning, and AI-ready data products Optimize data engineering capabilities to ensure performance and long-term business value Apply best practices for data security, privacy, and compliance Harness serverless computing and microservices to build resilient, scalable, and trustworthy data pipelines Who this book is for If you are a data engineer, ETL developer, or big data engineer who wants to master the principles and techniques of data engineering, this book is for you. A basic understanding of data engineering concepts, ETL processes, and big data technologies is expected. This book is also for professionals who want to explore advanced data engineering practices, including scalable data solutions, agile software development, and cloud-based data processing pipelines.