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

Preparing the prerequisites before developing our data warehouse

Before starting our practice exercise, let's carry out these small but important steps for authentication purposes. In this section, we will do the following:

  1. Access Cloud Shell
  2. Check our credentials using gcloud info
  3. Initialize our credentials using gcloud init
  4. Download example code and datasets from git
  5. Upload data to GCS from git

Let's look at each of these steps in detail.

Step 1: Access your Cloud shell

Revisit Chapter 2, Big Data Capabilities on GCP, if you haven't accessed your cloud shell in the GCP console.

Step 2: Check the current setup using the command line 

We want to check our current setup in Cloud Shell. To do that in Cloud Shell, type the following:

#gcloud info

Click authorize if prompted.

This command will give you information about installed components, Python versions, directories, and much more information besides. But what...