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

Exercise – Building a data lake on a Dataproc cluster

In this exercise, we will use Dataproc to store and process log data. Log data is a good representation of unstructured data. Organizations often need to analyze log data to understand their users' behavior. 

In the exercise, we will learn how to use HDFS and PySpark using different methods. In the beginning, we will use Cloud Shell to get a basic understanding of the technologies. In the later sections, we will use Cloud Shell Code Editor and submit the jobs to Dataproc. But for the first step, let's create our Dataproc cluster.

Creating a Dataproc cluster on GCP

To create a Dataproc cluster, access your navigation menu and find Dataproc. You will find the CREATE CLUSTER button, which leads to this Create a cluster page:

Figure 5.2 – Create a cluster page

There are many configurations in Dataproc. We don't need to set everything. Most of them are optional. For...