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Key benefits

Understand data engineering concepts, the role of a data engineer, and the benefits of using GCP for building your solution

Learn how to use the various GCP products to ingest, consume, and transform data and orchestrate pipelines

Discover tips to prepare for and pass the Professional Data Engineer exam

Description

With this book, you'll understand how the highly scalable Google Cloud Platform (GCP) enables data engineers to create end-to-end data pipelines right from storing and processing data and workflow orchestration to presenting data through visualization dashboards. Starting with a quick overview of the fundamental concepts of data engineering, you'll learn the various responsibilities of a data engineer and how GCP plays a vital role in fulfilling those responsibilities. As you progress through the chapters, you'll be able to leverage GCP products to build a sample data warehouse using Cloud Storage and BigQuery and a data lake using Dataproc. The book gradually takes you through operations such as data ingestion, data cleansing, transformation, and integrating data with other sources. You'll learn how to design IAM for data governance, deploy ML pipelines with the Vertex AI, leverage pre-built GCP models as a service, and visualize data with Google Data Studio to build compelling reports. Finally, you'll find tips on how to boost your career as a data engineer, take the Professional Data Engineer certification exam, and get ready to become an expert in data engineering with GCP. By the end of this data engineering book, you'll have developed the skills to perform core data engineering tasks and build efficient ETL data pipelines with GCP.

Who is this book for?

This book is for data engineers, data analysts, and anyone looking to design and manage data processing pipelines using GCP. You'll find this book useful if you are preparing to take Google's Professional Data Engineer exam. Beginner-level understanding of data science, the Python programming language, and Linux commands is necessary. A basic understanding of data processing and cloud computing, in general, will help you make the most out of this book.

What you will learn

Load data into BigQuery and materialize its output for downstream consumption

Build data pipeline orchestration using Cloud Composer

Develop Airflow jobs to orchestrate and automate a data warehouse

Build a Hadoop data lake, create ephemeral clusters, and run jobs on the Dataproc cluster

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

Use Dataflow to perform ETL on streaming data

Unlock the power of your data with Data Studio

Calculate the GCP cost estimation for your end-to-end data solutions

What do you get with Print?

Instant access to your digital copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Redeem a companion digital copy on all Print orders

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

Google Cloud Certified Professional Cloud Network Engineer Guide

$48.99

$64.99

Data Engineering with Google Cloud Platform

$69.99

Total $ 183.97

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Kyle Malone May 29, 2022

As a data analyst looking to expand my data engineering skills, I have been looking for a book on this topic for a while now. It's exactly what I needed to better understand how to write ETL jobs using GCP.The code in Github has a few errors, but if you know basic Python you should be able to spot and correct the mistakes fairly easily. Overall, this books has helped me a ton. I would definitely purchase it again.

Amazon Verified review

Behnam Jun 19, 2022

This book helped me to get started working with GCP in an organized way, now I have the confidence to do some real projects on GCP.

cloud-learner Jun 28, 2024

well written and easy to understand!

Om S May 31, 2022

Google Cloud Platform had always been pioneered when it comes to big data and AI. This platform is amazingly simple for developers and the author has done a great job putting everything together step by step to facilitate readers' and users' experience.The book is divided into 12 solid chapters; the last chapter is related to those who are interested in getting certification and want to know more about it.[Chapter 1 ] - Explains fundamentals of data engineering, ETL concept in data engineering and data life cycle, the difference between ETL and ELT, etc. It also explains ETL, big data, and distributed systems in short.[Chapter 2] - This is more related to big data products and their capability, GCP console, cloud shell, and cloud editor are needed and explained in detail with other associated services. The author has explained serverless services or fully managed services. Most of the cloud companies want clients/users to be on and opt serverless side of the story![Chapter 3] - Cover Google Cloud Storage and BigQuery console, Different kind of scenarios the author has presented give a different aspect of the data modeling and understanding of the BigQuery operations. You will learn design data modeling for BigQuery. Diagram and code to cover your practice that is your key.[Chapter 4] - In this chapter automating tasks, jobs, and how to handle their dependencies has been explained. Cloud Composer, the introduction of the open-source tool “Airflow” and data pipeline for BigQuery data warehouse. [Chapter 5] - If you know and have an idea about Spark and PySpark you will be enjoying this chapter! Developing Spark ETL from GCS to BigQuery is a fun part. Brief Intro to Dataproc which is Data Lake, building a data lake on a Dataproc cluster, creating and running jobs on a Dataproc cluster, the concept of the ephemeral cluster, using Dataproc and Cloud Composer is explained. My suggestion is to learn HDFS and Spark a bit in detail, this is an important chapter.[Chapter 6] – You will learn about streaming data and how to handle incoming data as soon as data is created using the Pub/Sub publisher client.[Chapter 7] - Data Studio for visualizing data with connectivity to BigQuery. Data studio explorer gives a lot of options to make charts/aggregations and visualize your data.[Chapter 8] - No one understands better than Google Cloud about Machine Learning and Artificial Intelligence. In this chapter, author has a provided bird eye view of ML, MLOps, some pre-built GCP models as a service, and deploying ML pipelines with Vertex AI are highlights. If you like ML you will be enjoying this chapter. Different Exercises make this a more fun chapter. [Chapter 9 ] IAM, project structure, and BigQuery ACLs, controlling user access/ infrastructure as code. You will also learn the power of Terraform but my intake is practicing “Terraform” a bit outside of this book is more fun and you will see new challenges. [Chapter 10] - This chapter cover cost strategy/saving money and also highlights how to estimate the overall data solution using GCP. End-to-end data solution cost with tools and tips for optimizing services. Happy clients bring good business![Chapter 11] - Continuous integration and continuous deployment (CI/CD) on Google Cloud Platform for Data Engineers, explains the concept of CI/CD and its relevance to data engineers. Without CI/CD cloud is like a handicap! If you have used that before you will understand its importance.[Chapter 12 ] – As I explained in the beginning it's all about Boosting Your Confidence as a Data Engineer, and preparing you for the GCP certification. Finally, I would conclude it as a great book. Nice references/URL and summary for revision.For a new version of the book, I would be expecting more questions and case studies for readers.

Yashar Mansouri Jun 26, 2022

Data Engineering with GCP by Adi Wijaya is a hands-on-book that covers processes such as Data Warehousing , ETL/ELT, and workflow management through the use of Google Cloud Platform's tech stack.Services such as IAM & Admin, BigQuery, CloudSQL, Cloud Storage, Composer, Dataproc, Pub/Sub, and Dataflow are covered in multiple chapters of the book with hands-on or coding examples through Cloud Console, Cloud Shell scripting, and even Python code. Most examples are presented by trying to answer business requirements for real world scenarios.What I specifically liked is that the author also covers the concepts around data engineering processes such as comparing the Inmon and Kimball Data Warehousing methods and when to use each as well as user and project management and the optimal cost strategy when using the tools provided by GCP.

Data Engineering with Google Cloud Platform: A practical guide to operationalizing scalable data analytics systems on GCP

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