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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

Summary

In this chapter, we learned about how CI/CD works in GCP services. More specifically, we learned about this from the perspective of a data engineer. CI/CD is a big topic by itself and is more mature in the software development practice. But lately, it's more and more common for data engineers to follow this practice in big organizations.

We started this chapter by talking about the high-level concepts and ended it with an exercise that showed how data engineers can use CI/CD in a data project. In the exercises, we used Cloud Build, Cloud Source Repository, and Google Container Registry. Understanding these concepts and what kind of technologies are involved were the two main goals of this chapter. If you want to learn more about DevOps practices, containers, and unit testing, check out the links in the Further reading section.

This was the final technical chapter in this book. If you have read all the chapters in this book, then you've learned about all the...

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