Book Image

Data Engineering with Google Cloud Platform

By : Adi Wijaya
3 (1)
Book Image

Data Engineering with Google Cloud Platform

3 (1)
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)
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 what the cloud is

Renting someone else's server: this definition of the cloud is my favorite, very simple, to the point, definition of what the cloud really is. So as long as you don't need to buy your own machine to store and process data, you are using the cloud. 

But increasingly, after some leading cloud providers such as Google Cloud having gained more traction and technology maturity, the terminology is becoming representative of sets of architecture, managed services, and highly scalable environments that define how we build solutions. For data engineering, that means building data products using collections of services, APIs, and trusting the underlying infrastructure of the cloud provider one hundred percent.

The difference between the cloud and non-cloud era

If we want to compare the cloud with the non-cloud era from a data engineering perspective, we will find that almost all the data engineering principles are the same. But from a technology...