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

Practicing developing a data warehouse

Now we are set and ready to build our first data warehouse. We will proceed with the help of two scenarios. Each scenario will have different learning purposes.

In the first scenario, we are going to focus on how to use the tools. After understanding the tools, in the second scenario, we will focus on the principles. Principles here mean that even though we know how to use the tools, there are many possibilities for using them. If we are talking about principles, there is no right or wrong answer. What we can do is to learn from common patterns and a number of theories. 

We will use the San Francisco bike-sharing dataset. The dataset relates to a bike-sharing company. The company records the trip data of its members. Each bike trip contains information about the stations, and lastly, each station is located in certain regions. This dataset is very simple compared to a real-world data warehouse, but for practice purposes, this is a good...