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

The MLOps landscape in GCP

In this section, let's learn what are GCP services related to MLOps. But before that, let's first understand what MLOps is.

Understanding the basic principles of MLOps

When we created the ML model in the previous section, we created some ML code. I found that most ML content and its discussion on the public internet is about creating and improving that part of ML. Some examples of typical topics include how to create a Random Forest model, ML regression versus classification, boosting ML accuracy with hyperparameters, and many more. 

All of the example topics mentioned previously are part of creating ML code. In reality, ML in a real production system needs a lot more than that. Take a look at the following diagram for the other aspects:

Figure 8.4 – Various ML aspects that ML code is only a small part of

As you can see, it's logical to have the other aspects in an ML environment. For example, in...