May 1, 2024
Updated June 29, 2025
18 minute read
Cloud Composer is a managed Apache Airflow platform on Google Cloud. It allows you to author, schedule, and monitor your machine learning pipelines. With Cloud Composer, you can focus on building and operationalizing your pipelines, without having to worry about the underlying infrastructure.
Why Learn Cloud Composer?
Cloud Composer can be a valuable tool for data scientists and machine learning engineers. It can help you to:
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Find a path to becoming a Cloud Composer. Learn more at:
OpenCourser.com/topic/vkmhk6/cloud
Reading list
We've selected 24 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Cloud Composer.
Comprehensive guide to building and maintaining data pipelines using Apache Airflow, the technology underlying Cloud Composer. It covers essential concepts, common usage patterns, and best practices for deploying Airflow in production environments. It's highly valuable as both a learning resource for beginners and a reference for experienced practitioners, solidifying understanding of DAGs and workflow orchestration.
Provides a practical guide to building data pipelines and analytics solutions on Google Cloud Platform, with specific coverage of Cloud Composer for workflow orchestration. It helps solidify understanding of how Cloud Composer fits into the broader GCP data ecosystem and is highly relevant for those working on GCP. The second edition covers recent updates and is useful for both learning and reference.
Focusing on practical implementation and scaling, this book delves into best practices for using Apache Airflow. It is particularly useful for those looking to deepen their understanding of operating Airflow in production and tackling contemporary challenges like migration and multi-tenancy. is valuable for solidifying understanding of robust Airflow deployments.
Lays a strong foundation in the core concepts of data engineering, including data lifecycle, architecture, and orchestration. While not specific to Cloud Composer or GCP, it provides essential prerequisite knowledge and a broad understanding of the field. It's considered a must-read for aspiring and current data engineers and helps solidify understanding of the principles behind data pipelines.
Guides readers in building end-to-end data science pipelines on GCP, including data ingestion, processing, and machine learning. It demonstrates how various GCP services, which can be orchestrated by Cloud Composer, work together. The second edition covers contemporary practices and is valuable for understanding the application of workflow orchestration in data science on GCP.
This study guide covers the breadth of services and topics relevant to the Google Cloud Professional Data Engineer certification, including Cloud Composer. It's excellent for gaining a broad understanding of the GCP data ecosystem and how Cloud Composer fits within it, and helps solidify knowledge for those preparing for certification.
Considered a modern classic, this book explores the fundamental trade-offs and concepts behind designing reliable, scalable, and maintainable data systems. While not directly about Cloud Composer, the principles discussed are crucial for anyone building data pipelines and understanding the underlying infrastructure. It provides deep, foundational knowledge that solidifies understanding of data systems architecture.
Comprehensive guide to using Airflow for machine learning. It covers topics such as data engineering, data science, and machine learning.
Python is the primary language for writing Apache Airflow DAGs, making proficiency in Python essential for Cloud Composer users. focuses on using Python for data engineering tasks, including building data pipelines. It provides necessary prerequisite knowledge and helps solidify the programming skills needed for implementing workflows.
Focusing specifically on the construction of ML pipelines, this book is highly relevant given that orchestrating ML workflows common use case for Cloud Composer. It provides practical guidance on building these pipelines, complementing the workflow orchestration aspect covered by Airflow-specific books.
Discusses the design and architecture of modern data and machine learning platforms, which often utilize workflow orchestration tools like Cloud Composer. It covers contemporary architectural patterns and provides valuable context for understanding where Cloud Composer fits in larger data initiatives, deepening understanding for architects and senior engineers.
MLOps contemporary and increasingly important field, and this book provides practical guidance on operationalizing machine learning models, including on Google Cloud. Since Cloud Composer is often used to orchestrate ML pipelines, this book offers valuable insights into related workflows and best practices, deepening understanding of a key Cloud Composer use case.
BigQuery key data warehouse service on GCP often used in conjunction with Cloud Composer pipelines. provides in-depth knowledge of BigQuery. It's valuable for users who need to integrate Cloud Composer workflows with BigQuery, offering deep understanding of a critical component in many GCP data solutions.
This concise reference provides a quick overview of data pipeline concepts, design considerations, and common patterns. It's a useful companion for reinforcing understanding and quickly looking up key information related to data movement and processing, which are fundamental to Cloud Composer workflows.
ETL (Extract, Transform, Load) core pattern in data engineering pipelines orchestrated by Cloud Composer. This report provides an overview of modern ETL processes and challenges. It's valuable for gaining foundational and prerequisite knowledge about the data transformations that often occur within Airflow DAGs.
A classic in the data warehousing field, this book provides in-depth coverage of ETL principles and techniques. While older, the foundational concepts of data extraction, transformation, and loading remain highly relevant to data pipelines built with Cloud Composer. It's valuable for historical context and deep understanding of ETL patterns.
Building on Python fundamentals, this book offers intermediate to advanced tips and techniques for writing more effective and idiomatic Python code. This is valuable for writing complex and optimized Airflow DAGs, deepening the programming skills required for advanced workflow development.
Another classic Kimball Group book, focusing specifically on star schema design for data warehouses. Understanding dimensional modeling is crucial for building ETL pipelines that load data into data warehouses, a common task orchestrated by Cloud Composer. Valuable for deep, foundational knowledge in data warehousing.
Another foundational book by the Kimball Group on the process of building a data warehouse. Understanding the steps and considerations in data warehousing projects provides crucial context for designing and implementing the ETL/ELT pipelines orchestrated by Cloud Composer.
Data Mesh contemporary architectural paradigm for managing data in distributed domains. While theoretical, it influences the design of data platforms and pipelines. Understanding Data Mesh concepts provides valuable context for how workflow orchestration tools might be used in modern decentralized data architectures.
For those new to Python, this book provides a fast-paced, project-based introduction to the language. Since Airflow DAGs are written in Python, a solid understanding of Python fundamentals necessary prerequisite. helps build that foundational programming knowledge.
Data modeling foundational skill for data engineering and designing effective data pipelines. provides a simple, step-by-step approach to understanding data modeling concepts, which is prerequisite knowledge for designing the data structures processed by Cloud Composer workflows.
While not directly about data engineering or Cloud Composer, writing clean, maintainable Python code is essential for developing robust Airflow DAGs. provides fundamental principles of software craftsmanship that are highly applicable to writing high-quality, testable, and understandable workflow definitions.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/vkmhk6/cloud
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