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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've gone through a lot of practice in terms of how to use BigQuery to build a data warehouse. In general, we've covered the three main aspects of how to use the tools, how to load the data to BigQuery, and the data modeling aspect of a data warehouse.

By following all the steps in this chapter, you will have a better understanding of the data life cycle and you will understand that data moves from place to place. We also practiced the ELT process in this chapter, extracting data from the MySQL database, loading it to BigQuery, and doing some transformations to answer business questions. And on top of that, we did it all on a fully managed service in the cloud, spending zero time worrying about any infrastructure aspects.

By way of a footnote for this chapter, I want to remind you that, even though we have covered the common practice of using BigQuery, we haven't covered all of its features. There are a lot of other features in BigQuery...

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