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

Summary 

In this chapter, we learned how to create an ML model. We learned that creating ML code is not that difficult and that the surrounding aspects are what make it complex. On top of that, we also learned about some basic terminologies such as AutoML, pre-built models, and MLOps.  

As I mentioned in the introduction, ML is not a core skill that a Data engineer needs to have. But understanding this topic will give a data engineer a bigger picture of the whole data architecture. This way, you can imagine and make better decisions when designing your core data pipelines. 

This chapter is the end of our big section on Building Data Solutions with GCP Components. Starting from Chapter 3, Building a Data Warehouse in BigQuery, to Chapter 8, Building Machine Learning Solutions on Google Cloud Platform, we've learned about all the fundamental principles of Data Engineering and how to use GCP services. At this point, you are more than ready to build a data...