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Data Engineering with Google Cloud Platform

Data Engineering with Google Cloud Platform - Second Edition

By : Adi Wijaya
4.5 (6)
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Data Engineering with Google Cloud Platform

Data Engineering with Google Cloud Platform

4.5 (6)
By: Adi Wijaya

Overview of this book

The second edition of Data Engineering with Google Cloud builds upon the success of the first edition by offering enhanced clarity and depth to data professionals navigating the intricate landscape of data engineering. Beyond its foundational lessons, this new edition delves into the essential realm of data governance within Google Cloud, providing you with invaluable insights into managing and optimizing data resources effectively. Written by a Data Strategic Cloud Engineer at Google, this book helps you stay ahead of the curve by guiding you through the latest technological advancements in the Google Cloud ecosystem. You’ll cover essential aspects, from exploring Cloud Composer 2 to the evolution of Airflow 2.5. Additionally, you’ll explore how to work with cutting-edge tools like Dataform, DLP, Dataplex, Dataproc Serverless, and Datastream to perform data governance on datasets. By the end of this book, you'll be equipped to navigate the ever-evolving world of data engineering on Google Cloud, from foundational principles to cutting-edge practices.
Table of Contents (19 chapters)
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1
Part 1: Getting Started with Data Engineering with GCP
4
Part 2: Build Solutions with GCP Components
11
Part 3: Key Strategies for Architecting Top-Notch Solutions

Exercise – practicing ML code using Python

In this section, we will use some of the terminologies we provided in the previous section and practice creating a quite simple ML solution using Python. The focus is for us 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 of 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 our example use case, we will predict if a credit card customer fails 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 exercise...

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Programming languages
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Data Engineering with Google Cloud Platform
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