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

Chapter 8: Building Machine Learning Solutions on Google Cloud Platform

The first machine learning (ML) solution came from the 1950s era. And I believe most of you know that in recent years, it's become very popular. It's undeniable that the discussion of artificial intelligence (AI) and ML is one of the hottest topics in the 21st century. There are two main drivers of this. One is the advancement in the infrastructure, while the second is data. This second driver brings us, as data engineers, into the ML area. 

In my experience discussing ML with data engineers, there are two different reactions – either very excited or totally against it. Before you lose interest in finishing this chapter, I want to be clear about what we are going to cover. 

We are not going to learn about ML from any historical stories nor the mathematical aspects of it. Instead, I am going to prepare you, as data engineers, for potential ML involvement in your GCP environment....