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 10: Cost Strategy in GCP

This chapter will cover one of the most frequently asked questions from stakeholders – the solution's cost. Each GCP service has different pricing mechanisms. In this chapter, we will look at what valuable information you will need for calculating cost.

On top of that, we will have a section dedicated to BigQuery. We will discuss the difference between two options for the BigQuery pricing models – on-demand and flat-rate. Finally, we will revisit the BigQuery features for partitioned and clustered tables. Understanding these features can optimize a lot of your future costs in BigQuery.

The following topics will be covered in this chapter: 

  • Estimating the cost of your end-to-end data solution in GCP
  • Tips for optimizing BigQuery using partitioned and clustered tables