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Key benefits

Get up to speed with data governance on Google Cloud

Learn how to use various Google Cloud products like Dataform, DLP, Dataplex, Dataproc Serverless, and Datastream

Boost your confidence by getting Google Cloud data engineering certification guidance from real exam experiences

Purchase of the print or Kindle book includes a free PDF eBook

Description

The second edition of Data Engineering with Google Cloud builds upon the success of the first edition by offering enhanced clarity and depth to data professionals navigating the intricate landscape of data engineering. Beyond its foundational lessons, this new edition delves into the essential realm of data governance within Google Cloud, providing you with invaluable insights into managing and optimizing data resources effectively. Written by a Data Strategic Cloud Engineer at Google, this book helps you stay ahead of the curve by guiding you through the latest technological advancements in the Google Cloud ecosystem. You’ll cover essential aspects, from exploring Cloud Composer 2 to the evolution of Airflow 2.5. Additionally, you’ll explore how to work with cutting-edge tools like Dataform, DLP, Dataplex, Dataproc Serverless, and Datastream to perform data governance on datasets. By the end of this book, you'll be equipped to navigate the ever-evolving world of data engineering on Google Cloud, from foundational principles to cutting-edge practices.

Who is this book for?

Data analysts, IT practitioners, software engineers, or any data enthusiasts looking to have a successful data engineering career will find this book invaluable. Additionally, experienced data professionals who want to start using Google Cloud to build data platforms will get clear insights on how to navigate the path. Whether you're a beginner who wants to explore the fundamentals or a seasoned professional seeking to learn the latest data engineering concepts, this book is for you.

What you will learn

Load data into BigQuery and materialize its output

Focus on data pipeline orchestration using Cloud Composer

Formulate Airflow jobs to orchestrate and automate a data warehouse

Establish a Hadoop data lake, generate ephemeral clusters, and execute jobs on the Dataproc cluster

Harness Pub/Sub for messaging and ingestion for event-driven systems

Apply Dataflow to conduct ETL on streaming data

Implement data governance services on Google Cloud

What do you get with Print?

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Download this book in EPUB and PDF formats

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Frequently bought together

Data Engineering with Google Cloud Platform

€31.99

Google Machine Learning and Generative AI for Solutions Architects

€37.99

Database Design and Modeling with Google Cloud

€26.99

Total € 96.97

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Amazon verified reviews

Steve Young Jun 21, 2024

Google Cloud Platform can be a very broad topic and contains many different products and services, yet this author was able to articulate on data engineering within the platform in a way that was much less dense than other books I’ve read. I am a data engineer and work with GCP on a daily basis. It was a pretty easy read while containing a lot of insightful and useful information about building data pipelines and other necessary activities of data engineering. The author also provided good color on how the platform is often used in particular industries which I found both useful and interesting. This book is a must-read if you have an interest in becoming a more functional and knowledgeable data engineer using GCP.

Amazon Verified review

SHASHI ANANTH Jun 12, 2024

One of the best books I have ever read, author is incredible in articulating Data warehousing, Data Engineering concepts, scenario are nicely explained , GCP data engineering tools are well explained with detailed steps. overall very nice book to learn Data engineering on Google cloud platform.

Daniel J. Hampton III Jun 17, 2024

I have been struggling to find a book that covered comprehensive big picture concepts as well as technical details and I think this book balances it quite well.

Johnnie Sep 15, 2024

Many concepts are covered (batch and streaming data pipeline creation, job orchestration, data governance and cost strategies) - as well as GCP cloud data storage options (with discussions in data warehouse design using BigQuery).The book went into more complex data engineering concepts in GCP such as ephemeral clusters, Dataproc (examining Hadoop, Spark and Dataframe concepts) and CI/CD practices.Note:For the curious minded engineer who ask, “dude … you mentioned ephemeral clusters. What’s the difference in ephemeral and persistent clusters???”Good question! With Persistent clusters there always is some infrastructure running. But with ephemeral clusters the clusters are created, exist for the time it takes for jobs to complete, and then cease to exist when they are brought downHow about transient clusters? I’ll leave that research up to you!“Lots of examples and exercises” are provided that enable a “hands on experience” for the reader to engage for greater understanding.This book provides data engineers with the concepts, hands on activities and guidance necessary to navigate the Google Cloud Platform (GCP).

mayanktripathi4u Sep 27, 2024

This book offers a comprehensive exploration of data engineering principles, specifically in the context of Google Cloud Platform (GCP). Aimed at both beginners and intermediate data engineers, it serves as an excellent resource for those looking to understand the fundamentals of building scalable data pipelines using GCP services. The book is particularly well-suited for data engineers, cloud architects, and IT professionals seeking to build robust, scalable data pipelines using Google Cloud’s services.What I liked:One of the most valuable aspects of this book is its structured approach. Adi Wijaya begins by laying a solid foundation, introducing readers to essential tools such as BigQuery, Cloud Storage, and Cloud Dataflow. From there, he builds upon that knowledge with more advanced topics like real-time data processing and machine learning integration, making it accessible for readers with varying levels of experience.The hands-on tutorials are another highlight, offering step-by-step instructions that allow readers to practice and implement what they've learned. This practical emphasis makes complex topics easier to grasp, particularly for those who prefer learning by doing. The author also includes command-line tools like gcloud and gsutil for interacting with Google Cloud services, providing readers with real-world experience in managing cloud resources. Additionally, the author does an excellent job showcasing real-world use cases, allowing readers to understand how these tools are applied in professional data engineering settings.Things which are missing as per my opinion:Although the book is packed with useful information, it may feel fast-paced for absolute beginners to cloud computing. Some prior understanding of cloud concepts would be beneficial to fully grasp the more advanced sections. Additionally, while the book provides a detailed look into GCP, readers looking for cross-platform comparisons (e.g., AWS or Azure) won’t find such insights here.Final Thoughts:Overall, "Data Engineering with Google Cloud Platform" is a highly valuable resource for anyone looking to master data engineering within GCP. Adi Wijaya delivers a balanced mix of theory and practical application, making it an ideal read for aspiring and practicing data engineers. Whether you're developing pipelines, optimizing workflows, or integrating machine learning, this book provides the knowledge you need to excel in GCP’s data ecosystem.

Data Engineering with Google Cloud Platform: A guide to leveling up as a data engineer by building a scalable data platform with Google Cloud , Second Edition

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