Data Engineering with Google Cloud Platform: A guide to leveling up as a data engineer by building a scalable data platform with Google Cloud, 2nd Edition

June 6, 2024 Books
Data Engineering with Google Cloud Platform: A guide to leveling up as a data engineer by building a scalable data platform with Google Cloud, 2nd Edition

English | 2024 | ISBN: 978-1835080115 | 476 Pages | PDF, EPUB | 68 MB

Data Engineering with Google Cloud Platform – Second Edition

Become a successful data engineer by building and deploying your own data pipelines on Google Cloud, including making key architectural decisions

Key Features

  • 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

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 invaluable insights into managing and optimizing data resources effectively. Furthermore, 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.

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
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