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

Exercise – practicing ML code using Python

In this section, we will use some of the terminologies we provided in the previous section. We will practice creating a very simple ML solution using Python. The focus for us is to understand the steps and start using the correct terminologies.

For this exercise, we will be using Cloud Editor and Cloud Shell. I believe you either know or have heard that the most common tool for creating ML models for Data scientists is Jupyter Notebook. There are two reasons I choose to use the editor style. One, not many Data engineers are used to the notebook coding style. Second, using the editor will make it easier to port the files to pipelines.

For the example use case, we will predict if a credit card customer will fail to pay their credit card bill next month. I will name the use case credit card default. The dataset is available in the BigQuery public dataset. Let's get started.

Here are the steps that you will complete in this...