Data engineering with google cloud professional certificate

Learning Paths

The foundation of Professional Data Engineer mastery is with the real-world job role of the cloud data engineer. Along with relevant experience, the training in this learning path can help support your preparation.

For more information about the exam and to register for, and pass the official Google Cloud certification exam, visit cloud.google.com/certification/data-engineer.

Courses in this path

Along with relevant experience, the training in this learning path will help you prepare for the Professional Data Engineer (PDE) exam, better understand the areas covered by the exam, and navigate the recommended resources: https://cloud.google.com/certification/data-engineer.

For more information about the exam and to register for, and pass the official Google Cloud certification exam, visit cloud.google.com/certification/data-engineer.

Google Cloud can help solve your toughest problems and grow your business. With Google Cloud, their infrastructure is your infrastructure. Their tools are your tools. And their innovations are your innovations.

Join our learners and upskill in leading technologies

Learn how to design, build and operate powerful big data and machine learning solutions using Google Cloud Platform

13 hours

• 124 Lessons
• 7 Hands-On Labs
• 10 Course Quizzes
• 1 Practice Exam

About the course

Hello Cloud Gurus!

Data management, data analytics, machine learning and artificial intelligence are all hot topics. And who does these better than Google? Our Google Certified Professional Data Engineer course will help prepare you for the certification exam so you can take that next step in your Cloud career and demonstrate your proficiency in one of the most in-demand disciplines in the industry today.

The primary focus of this course is to prepare you for the GCP Professional Data Engineer certification exam. Along the way you’ll solidify your foundations in data engineering and machine learning, ensuring that by the end of the course you will be able to design and build data processing solutions, operationalize machine learning models and gain a working knowledge of relevant GCP data processing tools and technologies.

This course will teach you how to:

• Design, build and operationalize data solutions
• Process data streams in real-time
• Efficiently store and access data in the cloud
• Use the GCP pre-trained AI APIs (vision, speech and text)
• Train and operationalize ML models.

The Google Cloud Professional Data Engineer is for data scientists, solution architects, devops engineers and anyone wanting to move into machine learning and data engineering in the context of Google. Students will need to have some familiarity with the basics of GCP, such as: storage, compute and security; some basic coding skills (like Python); and a good understanding of databases. You do not need to have a background in data engineering or machine learning, but some experience with GCP is essential. This is an advanced certification and we strongly recommend that students take the Google Certified Associate Cloud Engineer exam before embarking on this course. However, anyone who is motivated and wants to understand how big data and machine learning is done on GCP will still find value with this course.

Keep being awesome, Cloud Gurus!

Lab Highlights

• Chapter 1 5 Lessons Introduction 13:03

  An Important Note About A Cloud Guru and Linux Academy Courses

  1:18

  Course Audience and Prerequisites

  3:04

  A Note About Demo Lessons

  0:54

• Chapter 2 2 Lessons Data Processing Fundamentals 15:22

  Data Processing Concepts

  8:33

  Data Processing Pipelines

  6:49

• Chapter 3 20 Lessons Storage and Databases 4:52:24

  Introduction to Data Storage in GCP

  7:26

  Data Transfer Services

  3:52

  Demo: Creating a Cloud SQL Instance and Loading Data

  8:18

  Managing Google Cloud SQL Instances

  1:00:00 Hands-On Lab

  Working with Data in Google Cloud SQL

  30:00 Hands-On Lab

  Exploring Cloud Firestore in Datastore Mode

  30:00 Hands-On Lab

  Cloud Run Data in GCS and Firestore

  30:00 Hands-On Lab

  Demo: Working with Cloud Spanner

  6:00

  Setting Up for Google Cloud Spanner

  30:00 Hands-On Lab

  Demo: Working with Cloud Memorystore

  10:30

  Comparing Storage Options

  5:14

  Chapter 3 Quiz

  15:00 Quiz

• Chapter 4 5 Lessons Big Data Ecosystem 34:05

• Chapter 5 9 Lessons Real Time Messaging with Pub/Sub 1:22:43

  Demo: Working with Cloud Pub/Sub

  10:38

  Demo: Cloud Pub/Sub Client Libraries

  9:54

  Demo: Loosely Coupled Services with Cloud Pub/Sub

  10:23

  Demo: Stream Data through Cloud Pub/Sub to BigQuery

  16:11

  Chapter 5 Quiz

  15:00 Quiz

• Chapter 6 11 Lessons Pipelines with Cloud Dataflow 2:04:34

  Dataflow Introduction

  5:36

  Dataflow Pipeline Concepts

  5:47

  Advanced Dataflow Concepts

  5:41

  Dataflow Security and Access

  6:14

  Demo: Working with Cloud Dataflow

  8:00

  Demo: Streaming Pipelines with Cloud Dataflow

  17:34

  Create Streaming Data Pipeline on GCP with Cloud Pub/Sub, Dataflow, and BigQuery

  45:00 Hands-On Lab

  Chapter 6 Quiz

  15:00 Quiz

• Chapter 7 8 Lessons Managed Spark with Cloud Dataproc 1:14:35

  Demo: Working with Cloud Dataproc

  5:52

  Demo: Cloud Dataproc with the GCS Connector

  8:04

  Chapter 7 Quiz

  15:00 Quiz

  Running a Pyspark Job on Cloud Dataproc Using Google Cloud Storage

  30:00 Hands-On Lab

• Chapter 8 9 Lessons NoSQL Data with Cloud Bigtable 1:12:51

  Bigtable Architecture

  6:45

  Demo: Working with Cloud Bigtable

  13:00

  Bigtable Schema Design

  7:27

  Bigtable Advanced Concepts

  6:42

  Demo: Loading and Querying Data with Cloud Bigtable

  10:54

  Chapter 8 Quiz

  15:00 Quiz

• Chapter 9 11 Lessons Data Analytics with BigQuery 1:27:57

  Partitioning and Clustering

  7:38

  BigQuery Monitoring and Logging

  3:01

  Machine Learning with BigQuery ML

  3:51

  Demo: Working with BigQuery

  10:03

  Demo: Advanced BigQuery Features

  14:45

  Chapter 9 Quiz

  15:00 Quiz

• Chapter 10 3 Lessons Exploration with Cloud Datalab 24:34

  Demo: Working with Cloud Datalab

  11:21

  Demo: Jupyter Notebooks in GCP

  9:36

• Chapter 11 5 Lessons Visualization with Cloud Data Studio 34:00

  Reporting and Business Intelligence

  2:55

  Introduction to Data Studio

  2:46

  Chapter 11 Quiz

  15:00 Quiz

• Chapter 12 4 Lessons Orchestration with Cloud Composer 26:45

  Cloud Composer Overview

  5:50

  Cloud Composer Architecture

  3:12

  Demo: Working with Cloud Composer

  13:52

  Advanced Cloud Composer

  3:51

• Chapter 13 6 Lessons Introduction to Machine Learning 55:15

  Machine Learning Introduction

  9:28

  Machine Learning Basics

  15:08

  Machine Learning Types and Models

  8:37

  Feature Engineering

  10:06

• Chapter 14 5 Lessons Machine Learning with TensorFlow 46:07

  Deep Learning with TensorFlow

  6:25

  Introduction to Artificial Neural Networks

  14:41

  Neural Network Architectures

  6:04

  Building a Neural Network

  3:57

  Chapter 14 Quiz

  15:00 Quiz

• Chapter 15 7 Lessons Using Pre-Trained ML Cloud APIs 1:02:27

  Demo: Working with Cloud ML APIs

  14:09

  Chapter 15 Quiz

  15:00 Quiz

• Chapter 16 5 Lessons Leveraging Auto ML Platform 30:07

  Introduction to AutoML

  3:12

  Language with AutoML

  4:09

  Structured Data with AutoML

  3:18

  Chapter 16 Quiz

  15:00 Quiz

• Chapter 17 3 Lessons Operationalizing Machine Learning Models 11:20

  Introduction to Operationalizing ML Models

  4:24

• Chapter 18 5 Lessons Data Security and Industry Regulation 25:37

  Security and Regulation Overview

  5:37

• Chapter 19 2 Lessons Dataprep 17:10

  Demo: Working with Cloud Dataprep

  13:38

• Chapter 20 10 Lessons Preparing for the Professional Data Engineer Exam 2:47:30

  Reference Architectures: Big Data

  8:33

  Reference Architectures: Artificial Intelligence and Machine Learning

  3:41

  Reference Architectures: Internet of Things

  3:57

  Reference Architectures: Mobile & Gaming

  4:03

  External Resources and Tutorials

  3:20

  Exam Guide Breakdown

  18:51

  What to Expect From the Exam

  2:51

  Thank You and Good Luck!

  0:59

  Keep Up to Date with GCP This Month

  1:15

  Google Certified Professional Data Engineer

  2:00:00 Quiz

What you will need

• Baseline knowledge of Google Cloud Platform (particularly in storage, compute and security)

• Basic coding skills (Python or Go preferable)

• A basic understanding of machine learning concepts

• Some familiarity with databases and how they work

• Foundational mathematical understanding (e.g. Algebra)

What are Hands-on Labs

What's the difference between theoretical knowledge and real skills? Practical real-world experience. That's where Hands-on Labs come in! Hands-on Labs are guided, interactive experiences that help you learn and practice real-world scenarios in real cloud environments. Hands-on Labs are seamlessly integrated in courses, so you can learn by doing.

Is Google professional data engineer certification worth it?

Which cloud certification is best for data engineer?

How do I become a Google Cloud Certified professional data engineer?

What does a Google Certified professional data engineer do?