Google Cloud Data Engineer

Google data engineers need to know how to correctly manage data, including collecting, transforming, and visualizing data. They’re also responsible for designing, building, and maintaining processing systems while also managing their security and stability. Prepare for the exam by following the Professional Data Engineer learning path.

Enrolment validity: Lifetime

About This Course

In this course, you’ll discover the big data capabilities of Google Cloud Platform (GCP), including its data processing and machine learning operations. This course covers serverless data analysis and machine learning models that are provisioned in Google Cloud Platform, and provides skills and knowledge that are valuable to any student learning to complete the Google Data Engineer certificate.

  • Length: 2 hours
  • Registration fee: $200 (plus tax where applicable)
  • Languages: English, Japanese.
  • Exam format: Multiple choice
  • Prerequisites: None
  • Recommended experience: 3+ years of industry experience including 1+ years designing and managing solutions using GCP.

As per Indeed, the average income of these engineers is about US$127,053 per year with an annual bonus of US$5,000.

Learning Objectives

How to pass the Google Cloud Professional Data Engineer Exam
Build scalable, reliable data pipelines
Apply multiple types of machine learning techniques to different use cases
Deploy machine learning models in production
Monitor data pipelines and machine learning models
Design scalable, resilient distributed data intensive applications
Use Cloud SQL and Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud

Target Audience

  • Data professionals who are responsible for provisioning and optimizing big data solutions, and data enthusiasts getting started with Google Cloud Platform.
  • Cloud engineers and architects who want to pass the Professional Data Engineer exam
  • Data engineers who want to learn about Google's advanced tools and services for data engineering
  • Data scientists and data engineers who want to understand machine learning concepts
  • Cloud application developers who want to use machine learning to build applications
  • Devops engineers who need to support data engineering pipelines and machine learning models


133 Lessons30h


Google Cloud Platform Concepts
Navigating Google Cloud Platform Services
Benefits of Google Cloud Platform
Comparing GCP and Other Models
Creating a GCP Account
Creating a Project
GCP BigQuery
Practical Exercise

Storage & Analytics

Analytics and Scaling

Network Data Processing Models


Dataproc Architecture

Dataproc Operations

Implementations with BigQuery for Big Data

Fundamentals of Big Query

APIs and Machine Learning

Dataflow Autoscaling Pipelines

Machine Learning with TensorFlow and Cloud ML

Engineering and Streaming Architecture

Streaming Pipelines and Analytics

Big Data and Security



86% off
Duration 30 hours
133 lectures

Material Includes

  • Online, Self Paced Learning
  • Lifetime Access
  • Flexible Learning Program
  • Extensive Content for Self-Learning
  • Practice Quizzes
  • Course Completion Certificate