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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

Planning a GCP project structure

After practicing a lot of exercises from the previous chapters, I believe you will be more familiar with GCP. From the exercises, you've learned about GCP services, their positioning, and how to use them. In this section, we will take a step back. We will look at those GCP services from a higher-level point of view. 

In all the previous exercises throughout the book, we used only one project. All the GCP services such as BigQuery, GCS buckets, Cloud Composer, and the other services that we used are enabled and provisioned in one project. For me, I have a project called packt-data-eng-on-gcp. The same from your side—you must have your own project, either using the default project or a new one that we created in Chapter 2, Big Data Capabilities on GCP. That's a good enough starting point for learning and development, but in reality, an organization usually has more than one project. There are many scenarios and variations...

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