Professional Level
7 Hands-on labs

Google Certified Professional Data Engineer

By Tim Berry

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

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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

Cloud Run Data in GCS and Firestore Create a Streaming Data Pipeline on GCP with Cloud Pub/Sub, Dataflow, and BigQuery Running a Pyspark Job on Cloud Dataproc Using Google Cloud Storage Exploring Cloud Firestore in Datastore Mode Managing Google Cloud SQL Instances Working with Data in Google Cloud SQL Setting Up for Google Cloud Spanner
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  • Chapter 1 5 Lessons Introduction 13:03

    An Important Note About A Cloud Guru and Linux Academy Courses

    1:18

    Welcome

    2:51

    About This Course

    4:56

    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

    Working with Data

    4:50

    Cloud Storage

    10:03

    Service Accounts

    6:07

    Data Transfer Services

    3:52

    Cloud SQL

    9:17

    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

    Cloud Firestore

    8:31

    Exploring Cloud Firestore in Datastore Mode

    30:00 Hands-On Lab

    Cloud Run Data in GCS and Firestore

    30:00 Hands-On Lab

    Cloud Spanner

    11:45

    Demo: Working with Cloud Spanner

    6:00

    Setting Up for Google Cloud Spanner

    30:00 Hands-On Lab

    Cloud Memorystore

    3:40

    Demo: Working with Cloud Memorystore

    10:30

    Comparing Storage Options

    5:14

    Exam Tips

    1:51

    Chapter 3 Quiz

    15:00 Quiz
  • Chapter 4 5 Lessons Big Data Ecosystem 34:05

    MapReduce

    6:22

    Hadoop & HDFS

    7:37

    Apache Pig

    3:09

    Apache Spark

    9:46

    Apache Kafka

    7:11
  • Chapter 5 9 Lessons Real Time Messaging with Pub/Sub 1:22:43

    Pub/Sub Concepts

    6:35

    Pub/Sub Basics

    3:59

    Demo: Working with Cloud Pub/Sub

    10:38

    Demo: Cloud Pub/Sub Client Libraries

    9:54

    Advanced Pub/Sub

    7:24

    Demo: Loosely Coupled Services with Cloud Pub/Sub

    10:23

    Demo: Stream Data through Cloud Pub/Sub to BigQuery

    16:11

    Exam Tips

    2:39

    Chapter 5 Quiz

    15:00 Quiz
  • Chapter 6 11 Lessons Pipelines with Cloud Dataflow 2:04:34

    Dataflow Introduction

    5:36

    Pipeline Lifecycle

    5:39

    Dataflow Pipeline Concepts

    5:47

    Advanced Dataflow Concepts

    5:41

    Dataflow Security and Access

    6:14

    Using Dataflow

    7:28

    Exam Tips

    2:35

    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

    Dataproc Overview

    3:22

    Dataproc Basics

    3:42

    Demo: Working with Cloud Dataproc

    5:52

    Advanced Dataproc

    6:20

    Demo: Cloud Dataproc with the GCS Connector

    8:04

    Exam Tips

    2:15

    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 Concepts

    6:06

    Bigtable Architecture

    6:45

    Bigtable Data Model

    4:38

    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

    Exam Tips

    2:19

    Chapter 8 Quiz

    15:00 Quiz
  • Chapter 9 11 Lessons Data Analytics with BigQuery 1:27:57

    BigQuery Basics

    5:31

    Using BigQuery

    13:10

    Partitioning and Clustering

    7:38

    Best Practices

    8:05

    Securing BigQuery

    4:32

    BigQuery Monitoring and Logging

    3:01

    Machine Learning with BigQuery ML

    3:51

    Exam Tips

    2:21

    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

    Datalab Concepts

    3:37

    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

    Data Distributions

    8:13

    Introduction to Data Studio

    2:46

    Charts and Filters

    5:06

    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

    Overfitting

    7:54

    Hyperparameters

    4:02

    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

    Cloud AI Cloud APIs

    4:20

    Vision

    6:49

    Video Intelligence

    5:01

    Language

    8:04

    Conversation

    9:04

    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

    Sight with AutoML

    4:28

    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

    Kubeflow

    2:50

    AI Platform

    4:06
  • Chapter 18 5 Lessons Data Security and Industry Regulation 25:37

    Security and Regulation Overview

    5:37

    IAM Best Practices

    6:12

    Data Security

    4:17

    Data Privacy

    3:58

    Industry Regulation

    5:33
  • Chapter 19 2 Lessons Dataprep 17:10

    Dataprep Overview

    3:32

    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.

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