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 use Data Studio using BigQuery as the data source. We learned how to connect the data, create charts in Explorer, and create reports for sharing the charts and information with other users. 

Through the exercises in this chapter, you have learned about not only how to create charts but also the point of view of your end users. In the exercises, you realized how important it is to create a proper data model in your datasets. Imagine if your tables didn't have proper naming conventions, weren't aggregated properly, or followed any other bad practices that can happen in a data warehouse. Since we already learned all the good data engineering practices and code from the previous chapters, it's now very easy for us to use our example tables to visualize things in Data Studio.

And lastly, as data engineers, we need to be the ones who understand and are aware of the cost implications in our data ecosystem. In this chapter...