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 about two different things. First, we learned about how to estimate the end-to-end data solution cost. Second, we understood how BigQuery partitioned and clustered tables play a significant role in the cost. 

These two topics are usually needed by data engineers in different situations. Understanding how to calculate the cost will help in the early stages of GCP implementation. This is usually a particularly important step for an organization to decide the future solution for the whole organization. 

The second topic usually occurs when you're designing BigQuery tables and at a time when you need to evaluate the running BigQuery solution. Even though it's obvious that using partitioned and clustered tables is beneficial, it's not a surprise in a big organization as many tables are not optimized and can be improved. 

Lastly, we performed an experiment using the three different tables. It proved that using the...