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Data Engineering with Google Cloud Platform

Data Engineering with Google Cloud Platform

By : Adi Wijaya
4.7 (12)
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Data Engineering with Google Cloud Platform

Data Engineering with Google Cloud Platform

4.7 (12)
By: Adi Wijaya

Overview of this book

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.
Table of Contents (17 chapters)
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1
Section 1: Getting Started with Data Engineering with GCP
4
Section 2: Building Solutions with GCP Components
11
Section 3: Key Strategies for Architecting Top-Notch Data Pipelines

Understanding IAM in GCP 

IAM is a central manager that manages who can access what—in other words, authorization. IAM manages all authorization within GCP. The concept is simple—you grant roles to accounts so that the accounts have the required permission to access specific GCP services. Here is a diagram for an account that needs to query a table in BigQuery:

Figure 9.1 – IAM roles, permissions, and GCP service correlation

In the example shown in the previous diagram, in order to access a BigQuery table, an account needs, at a minimum, two roles: data viewer and job user. These roles contain multiple permissions to specifically perform an operation in BigQuery. 

Let's go through each of the important terms that we use in the IAM space, as follows:

  • Account—An account in GCP can be divided into two—a user account and a service account:
    • User account—Your personal email is a user account. ...
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