Google Data Engineering Simplified: 7 Critical Aspects

Shubhnoor Gill • Last Modified: December 29th, 2022

Google Data Engineering Feature Image

Today, many Data Engineering workloads are built on Hadoop and Spark platforms, with Python, Scala, Java, and other programming languages used to run them. In recent years, you might have seen a significant increase in the number of organizations using Cloud Platforms to deploy their projects.

Sitting on mountains of potentially lucrative real-time data, these organizations require Data Engineers to use tools for handling data rapidly and efficiently. So, to fulfill their needs and achieve customer satisfaction, companies use Cloud Platforms like the Google Cloud Platform(GCP). These tools help in handling the data efficiently and perform other valuable Data Engineering processes.

In this article, you will learn Data Engineering with the Google Cloud Platform. You will discover a few unknown facts about the Google Cloud Platform and also learn about the key features of the Google Data Engineering Platform. Not only this, you will explore the Google Data Engineering tools that companies use. Finally, you would understand the steps to becoming a Google Professional Data Engineer. Read along to gain insights and understand more about GCP.

You might also love to check our article on Google Cloud ETL.

Table of Contents

What is Data Engineering?

Google Data Engineering
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Data Engineering is the foundation for the new world of Big Data. As companies become more reliant on data, the importance of Data Engineering continues to grow. Since 2012, Google searches for the phrase “Data Engineering” have tripled. Companies are discovering new ways to use data to their advantage. They use data to analyze the current status of their business, forecast the future, model their customers, avoid threats and develop new offerings. Data Engineering is the linchpin in all these activities.

Data Engineering is a field that deals with data analysis and activities such as obtaining and storing data from various sources. Then, take the data and clean them up so they may be used in other processes like Data Visualisations, Business Analytics, and Data Science solutions. Data Engineering ultimately aims at providing ordered and consistent data flow to permit the processing of data such as:

  • Training Machine Learning Models
  • Perform Exploratory Data Analysis
  • Populate fields with External Data in an application

It is imperative nowadays that enterprises require abundant Data Engineers to provide the foundations for effective Data Science projects in the context of full digital corporate transformations, the Internet of Things, and the race to become AI-drifty. Briefly, a Data Engineer is in charge of managing a large amount of data and sending this data into Data Science Pipelines.

What is Google Cloud Platform?

Google Data Engineering: Google Cloud
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Google Cloud Platform(GCP) is a robust Cloud Platform offered by Google that offers a suite of computing services to customers to access computer resources. The resources are housed in Google’s data centers around the world for free or on a pay-per-use basis. The GCP helps leverage to work more efficiently and gain more flexibility.

Google Cloud Platform is a part of Google Cloud, which includes:

  • Google Cloud Platform: GCP provides public cloud infrastructure for hosting web-based applications.
  • Google Workspace (G Suite): Google Workspace is a collection of cloud computing, productivity, and collaboration tools, software, and products developed and marketed by Google. Some of the tools are Gmail, Docs, Sheets, Slides, Meet, and many more.
  • Enterprise Versions of Android and Chrome OS: Chrome Enterprise provides IT Administrators with cloud-based management tools, interfaces with third-party companies, and 24/7 support. On the other hand, Android Enterprise is a Google-led service that allows developers to use Android devices and apps in the workplace. Moreover, it offers APIs and other tools to integrate support for Android into its Enterprise Mobility Management (EMM) solutions. 
  • Application Programming Interfaces (APIs) for Machine Learning and Enterprise Mapping Services: Google APIs are application programming interfaces (APIs) developed by Google that allow users to communicate with Google Services and integrate them with other services. Examples of products and APIs for Machine Learning include Cloud Vision API, AutoML, Recommendations AI, and many more.

GCP also provides Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software-as-a-Service (SaaS) environments. It also offers various kinds of services like: 

  • Compute
  • Networking
  • Storage and Databases
  • Artificial Intelligence (AI) / Machine Learning (ML)
  • Big Data
  • Identity and Security
  • Management Tools

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What is the Significance of Data Engineering With Cloud?

Cloud Computing is no more a relic of the past; it is a long-term solution for securing data in a flexible and scalable manner that may also save you money. Previously, Data Engineers were primarily concerned with handling data stored in Excel Spreadsheets and on local machines. As data grew in size and complexity, more tools were gradually added to cope with it, but the general landscape remained mostly unaltered until the development of “The Cloud.” 

Cloud Computing allows hardware and software products to coexist and scale remotely. These technologies work together to provide specialized services. Users can usually access, manage, and use the tools they need using a web interface without worrying about the hardware. 

Today, companies are realizing that insights from data can help to add more and more value to their users’ lives. So, business users value a fast, well-maintained Cloud Data Pipeline because it allows them to quickly answer all of their questions while serving up insights. Hence, this is where Cloud comes into the picture. For example, the Google Data Engineering platform offers a robust security strategy, a unique invoicing approach, and a significant reliance on data analytics for optimal performance.

Key Features of the Google Data Engineering Platform

Google Data Engineer: google Cloud
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In this section, you will understand the key features of the Google Data Engineering Platform that will help you to differentiate it from the other Public Cloud Providers in a variety of ways. Following are the few critical features of the Google Data Engineering Platform:

1) Google Data Engineering Feature: GCP Services

One of the major reasons why customers prefer GCP is due to its services and distributed applications model. GCP provides a range of computing services, including GCP Cost Management, Data Management, Web and Video Delivery over the web, and AI and Machine Learning Tools.

2) Google Data Engineering Feature: Security

The Google Security Model is based on more than 15 years of expertise keeping Google users safe while using their services. Google Cloud Platform enables your applications and data to run on the same secure network that Google has designed for itself.

3) Google Data Engineering Feature: Pricing

GCP, like most cloud providers, offers a monthly pay-as-you-go option. This means that your bill is depending on how much time you spend using the compute engine instances. Google, on the other hand, goes a step farther and charges per second with a one-minute minimum charge. When your company isn’t using Compute Engine, this helps you to save even more money, especially if you’re running short-term workloads or a dynamic web application.

4) Google Data Engineering Feature: Data Engineering and Big Data Services

Google additionally stands out due to its Google BigQuery-Powered GCP Data Analytics. You may process data in the Cloud with Big Data services to acquire answers to your most complicated questions. In addition, you may construct schemas, load data, generate queries, and export data.

3 Unknown Facts of Google Cloud Platform

In this section, you will uncover some interesting facts about the Google Cloud Platform. Some of those facts include:

  1. Google Cloud, or Google Cloud Platform (GCP), formerly known as App Engine, is a Cloud Computing service suite launched by Google in 2008. Moreover, Google has dedicated data centers that span a global network across more than 200 countries.
  1. GCP’s services are comparable to those of Amazon Web Services (AWS) and Microsoft Azure. Together with these two competitors, GCP is considered among the major Cloud providers, taking approximately 10 percent market share
  1. According to International Data Corporation (IDC), businesses using Google Cloud Platform are achieving 222% Return On Investment (ROI) over three years, 41% more efficient IT teams, 19% higher developer productivity, and 26% lower IT infrastructure costs. To read more about it, refer to IDC: Business Value of Google Cloud Platform.

Google Data Engineering Tools

In this section, you will take a glimpse of the 8 most commonly used Google Data Engineering tools that companies use. They are:

1) Google BigQuery 

Google Data Engineering - Google BigQuery Logo
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Google BigQuery is a Serverless, Fully Managed Cloud Data Warehouse offered by Google. It allows you to import, and manage petabytes of data. It offers rapid SQL queries and interactive analysis of massive datasets. Moreover, it also has built-in, powerful Machine Learning capabilities.

2) Google Cloud Data Fusion

Google Data Engineering - Google Cloud Data Fusion Logo
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Cloud Data Fusion is a Fully Managed, Cloud-Native Enterprise Data Integration Service that allows you to create and maintain data pipelines rapidly. It also supports a visual point-and-click interface that enables code-free deployment of ETL/ELT data pipelines.

3) Google Cloud Dataflow

Google Data Engineering - Google Cloud Dataflow Logo
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Dataflow is a Cloud-based data processing service that is used for batch and real-time data streaming. It allows developers to create processing pipelines for integrating, preparing, and analyzing massive data sets.

4) Google Cloud Dataproc

Google Data Engineering - Google Cloud Dataproc Logo
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Dataproc is a Managed Spark and Hadoop Service that enables batch processing, querying, streaming and Machine Learning. Dataproc Automation allows you to easily create clusters, manage them, and save money by turning clusters off when they aren’t in use.

5) Google Cloud Composer

Google Data Engineering - Google Cloud Composer Logo
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Cloud Composer is a Fully Managed Data Workflow Orchestration Tool that lets you write, schedule, and track pipelines. It is built on the Apache Airflow open source project and operated using Python.

6) Google Cloud Datalab

Google Data Engineering - Google Cloud Datalab Logo
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Cloud Datalab allows you to interactively explore, view, analyze, and transform data using familiar languages like Python and SQL. You can use notebooks with Python, TensorFlow Machine Learning, and Google Analytics, Google BigQuery, and Google Charts APIs.

7) Google Cloud Dataprep

Google Data Engineering - Google Cloud Dataprep Logo
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Cloud Dataprep is a data service that allows you to explore, clean, and prepare structured and unstructured data in the cloud. There is no requirement for infrastructure to deploy or manage because Dataprep is serverless and operates at any scale.

8) Google Cloud Data Studio

Google Cloud Data Studio Logo
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Google Data Studio is a Business Intelligence tool that helps you transform your data into completely flexible, easy-to-read, and share reports and dashboards. The Google Data Studio BigQuery connector allows you to use Google Data Studio to access data from Google BigQuery tables. You can also read about our in-depth tutorial of Google Data Studio.

Steps to Become a Google Professional Data Engineer

Google Data Engineering - Google Professional Data Engineer
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Google provides a golden opportunity to get yourself certified as a Professional Data Engineer. A Professional Data Engineer enables data-driven decision-making by collecting, transforming, and publishing data. Moreover, the Engineer can design, build, and monitor data processing systems and also be able to leverage, deploy, and continuously train pre-existing Machine Learning models.

In this section, you will understand the steps required to make your Professional Google Data Engineer journey successful.

1) Review the Exam Guide

The first and foremost step, while starting your preparation for any certification, is to review and analyze the exam guide and requirements. Google provides you with an exam guide that contains a complete list of topics that may be included in the exam. Review the exam guide to determine if your skills align with the topics on the exam. Refer here for the exam guide.

2) Start Learning 

The next step is to prepare for the exam. To do so, you should train yourselves and get all the skills required for the exam. You should explore online training, in-person classes, hands-on labs, and other resources from Google Cloud. In addition to this, you can get valuable exam tips and insights from Googlers and industry experts by attending the webinar. For learning resources, you can refer to Data Engineering & Analytics Courses.

3) Solve Sample Questions

To familiarize yourself with the format of exam questions and example content that may be covered on the Data Engineer exam, try solving the sample Data Engineering questions.

4) Schedule an Exam

Finally, after all the preparation and hard work you can register and select the option to take the exam remotely or at a nearby testing center.

Refer to this link for more information about Google Data Engineering Certification.


In this article, you learned about the significance of Data Engineering with the Cloud as well as the crucial role played by it. In addition to this, 4 critical features of the Google Data Engineering Platform were discussed. This article also highlighted the top 8 Google Data Engineering tools along with some unknown facts of GCP. You also learned the steps to become a successful Google Professional Data Engineer. Overall, Google Data Engineering is one aspect of Google that keeps on innovating. 

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