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

Chapter 3: Building a Data Warehouse in BigQuery

The power of a data warehouse is delivered when organizations combine multiple sources of information into a single place that becomes the single source of truth. The utopia of data analytics will be when every single business aspect in an organization relies on data. That condition will be met when all business decision makers know how to access data, trust the data, and can make decisions based on it. 

Unfortunately, most of the time, utopia is far removed from reality. There are many challenges along the way. Based on my experience, there are three main challenges – technology bottlenecks, data consistency, and the ability to serve multiple business purposes.

The preceding challenges are natural when we build a data warehouse. It's not limited to certain technologies and organizations. In this chapter, we will learn those challenges through two hands-on scenarios. We will mainly use BigQuery as the Google...