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 5: Building a Data Lake Using Dataproc

A data lake is a concept similar to a data warehouse, but the key difference is what you store in it. A data lake's role is to store as much raw data as possible without knowing first what the value or end goal of the data is. Given this key differentiation, how to store and access data in a data lake is different compared to what we learned in Chapter 3, Building a Data Warehouse in BigQuery.

This chapter helps you understand how to build a data lake using Dataproc, which is a managed Hadoop cluster in Google Cloud Platform (GCP) But, more importantly, it helps you understand the key benefit of using a data lake in the cloud, which is allowing the use of ephemeral clusters.

Here is the high-level outline of this chapter:

  • Introduction to Dataproc
  • Building a data lake on a Dataproc cluster
  • Creating and running jobs on a Dataproc cluster
  • Understanding the concept of the ephemeral cluster
  • Building an ephemeral...