IT Certification


Google Cloud Professional Data Engineer: Get Certified 2020

Build scalable, reliable data pipelines, databases, and machine learning applications.

4.39 (920 reviews)

Google Cloud Professional Data Engineer: Get Certified 2020


6 hours


Nov 2020

Last Update
Regular Price

What you will learn

How to pass the Google Cloud Professional Data Engineer Exam

Build scalable, reliable data pipelines

Choose appropriate storage systems, including relational, NoSQL and analytical databases

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

Migrate data warehouse from on-premises to Google Cloud

Evaluate and improve the quality of machine learning models

Grasp fundamental concepts in machine learning, such as backpropagation, feature engineering, overfitting and underfitting.


The need for data engineers is constantly growing and certified data engineers are some of the top paid certified professionals. Data engineers have a wide range of skills including the ability to design systems to ingest large volumes of data, store data cost-effectively, and efficiently process and analyze data with tools ranging from reporting and visualization to machine learning. Earning a Google Cloud Professional Data Engineer certification demonstrates you have the knowledge and skills to build, tune, and monitor high performance data engineering systems.

This course is designed and developed by the author of the official Google Cloud Professional Data Engineer exam guide and a data architect with over 20 years of experience in databases, data architecture, and machine learning. This course combines lectures with quizzes and hands-on practical sessions to ensure you understand how to ingest data, create a data processing pipelines in Cloud Dataflow, deploy relational databases, design highly performant Bigtable, BigQuery, and Cloud Spanner databases, query Firestore databases, and create a Spark and Hadoop cluster using Cloud Dataproc.

The final portion of the course is dedicated to the most challenging part of the exam: machine learning.  If you are not familiar with concepts like backpropagation, stochastic gradient descent, overfitting, underfitting, and feature engineering then you are not ready to take the exam. Fortunately, this course is designed for you. In this course we start from the beginning with machine learning, introducing basic concepts, like the difference between supervised and unsupervised learning. We’ll build on the basics to understand how to design, train, and evaluate machine learning models. In the process, we’ll explain essential concepts you will need to understand to pass the Professional Data Engineer exam. We'll also review Google Cloud machine learning services and infrastructure, such as BigQuery ML and tensor processing units.

The course includes a 50 question practice exam that will test your knowledge of data engineering concepts and help you identify areas you may need to study more.

By the end of this course, you will be ready to use Google Cloud Data Engineering services to design, deploy and monitor data pipelines, deploy advanced database systems, build data analysis platforms, and support production machine learning environments.

ARE YOU READY TO PASS THE EXAM? Join me and I'll show you how!




Preparing for the Google Cloud Professional Data Engineer Exam

Cloud Storage for Data Engineering

Introduction to Object Storage

Options for Loading Data

Access Controls for Cloud Storage

Lifecycle Policy Management

Using Cloud Storage Console

Exercise: Cloud Storage

Solution: Cloud Storage

Relational Databases - Cloud SQL

Introduction to Relational Databases

When to use Cloud SQL

Creating a Cloud SQL Database

Monitoring Cloud SQL

Exercise: Create a Cloud SQL Database

Solution: Create a Cloud SQL Database

Relational Databases - Cloud Spanner

When to use Cloud Spanner

Creating a Cloud Spanner Database

Cloud Spanner Performance Considerations

NoSQL Databases: Cloud Firestore

Introduction to Cloud Firestore & Document Databases

Entities and Kinds

Indexing in Cloud Firestore

Creating Entities

Querying Entities

Creating Kinds and Namespaces

Working with Transactions

Exercise: Create a Kind and Entities

Solution: Creating Kinds and Entities

NoSQL Databases: Bigtable

Introduction to Bigtable and Wide-Column Databases

Creating a Bigtable Instance

Designing Row-keys for Bigtable

Query Patterns and Denormalization

Designing for Time Series Data

Analytical Databases: BigQuery Data Management

Introduction to BigQuery and Analytical Databases

BigQuery Scalar Datatypes

BigQuery Nested and Repeated Fields

Querying Scalars, Nested and Repeated Fields

Exercise: Querying BigQuery Public Datasets

Solution: Querying BigQuery Public Datasets

Access Controls in BigQuery

Partitioning Tables

Clustering Partitioned Tables

Loading Data into BigQuery

Migrating a Data Warehouse

Assessing the Current State of a Data Warehouse

Schema and Data Transfer

Data Pipelines

Reporting and Analysis

Data Governance

Caching Data and Cloud Memorystore

Using Caching to Improve Performance

Cloud Memorystore Data Structures

When to use Cloud Memorystore

Cloud Pub/Sub and Data Pipelines

Introduction to Cloud Pub/Sub

Creating Topics and Subscriptions

Creating and Reading Messages

Exercise: Create a Topic, Publish Messages, Read Messages

Solution: Create a Topic Publish Message

Cloud Dataflow and Data Pipelines

Stream and Batch Processing with Cloud Dataflow

Running a Job in Cloud Dataflow

Analyzing a Failed Job in Cloud Dataflow

Monitoring Cloud Dataflow

Troubleshooting a Cloud Dataflow Pipeline

Cloud Dataproc and Data Pipelines

Introduction to Cloud Dataproc

Creating a Cloud Dataproc Cluster

Monitoring a Cloud Dataproc Cluster

Using Cloud Storage with Cloud Dataproc

Special Considerations in Distributed Systems

Hybrid and multi-cloud computing

Asychronous Messaging

Stream Processing

Data Consistency Models

Monitoring and Logging

Monitoring and Alerting with Cloud Monitoring

Logging with Cloud Logging

Creating an Alert

Install the Monitoring Agent on a Virtual Machine

Security and Compliance

Introduction to Identity Access Management

Resource Hierarchy

Predefined Roles

Custom Roles

Primitive Roles

IAM Best Practices

Ensuring Privacy with Data Loss Prevention API

Legal Compliance

Encryption At Rest and In Motion

Key Management

Exercise: Grant roles to users

Solution: Grant roles to users

Introduction to Machine Learning

3 Categories of Machine Learning Problems

2 Approaches to Building ML Models

Symbolic Machine Learning

Neural Networks and Deep Learning

Building ML Models

Features and Labels

Feature Engineering

Model Building

Model Evaluation

Gradient Descent and Backpropagation

Model Troubleshooting

Building Models in GCP

Using Pre-built ML Models

Deploying and Monitoring ML Models

Options for Deploying ML Models

Using GPUs and TPUs

Monitoring ML Models

Bias and Unfairness in ML Models

Analytical Databases: BigQuery ML

Introduction to Machine Learning in BigQuery

Creating a Regression Model in BigQuery

Evaluating a Regression Model in BigQuery

Using Model for Predictions in BigQuery

Exercise: Creating a Model in BigQuery

Solution: Creating an ML Model in BigQuery


Conclusion and Next Steps

Practice Exam

GCP Professional Data Engineer Practice Exam


Ivan10 March 2021

Good overview of GCP services, Data Engineering and even some Machine Learning. As for preparation to the certification, I found the course author's book more useful because the book goes deeper into the exam topics, which you can't expect from a 6-hour video course.

Richard21 February 2021

I was able to pass Google Data Engineer exam using notes from this course and the Book that was mentioned in this course. I would highly recommend this course to anyone who is preparing for the exam.

Sivanessen9 December 2020

It is very plain and reading through the text. I would be great if complex topics are explained with examples, use cases, visually animated and leveraging architecture diagram

Tom4 December 2020

Decent starting info but doesn't go into enough details. Good enough to get a baseline for each service to start out. Please, please, edit your videos more especially the gasping breath sounds. It makes it really hard to pay attention

Clemon4 December 2020

The Google Professional Data Engineer course answered a few questions I had, mainly around what exactly it entailed in regards to how they created and used the data to get the required results.

Gitanjali2 December 2020

The course is good. I will recommend this course who are seriously considering to appear for GCP Professional Data Engineer Certification. Before going through the course material, make sure that you understand the basic Data Processing services in GCP. The language used by the author is very technical and make sure that you understand the terminologies well before taking this course.

Charles24 November 2020

presentation graphics are too blurry even in full screen mode. Please refine these to be sharper at 720p.

Heidi14 November 2020

This is a great course with comprehensive lectures. I would highly recommend for anyone getting certified or wanting to become more confident with all of the data storage and processing solutions in GCP.

Kizito11 November 2020

The course is good. I had no particularity about what to expect because this is my first time exploration data engineering and in the cloud. I think that the instructor while he explains the concepts well, failed to make his slides communicate the same ideas. You can't derive understanding from reading what's on them except accompanied by his words and I think that's suboptimal approach. Slides should distill the ideas accurately and communicate them independently even though highly summarized.

Bharatagraj3 October 2020

This is a quick and handy reference. I will advice to try to make this course some thing like a Linux Academy one where lot of in depth explanations are done. Agreed that this is the first version of the course and expect this to be highly competitive for all the online GCP courses, Dan has a very good habit of using the topic reference from the Google List of Topics for the certification given in his book. But with the depth that Dan can handle and at the same time keep it crispy will eventually make this course the best one online. May be he might have that version on any other platform but keeping on UDEMY will help us a lot. You can consider a different charge/costing for that detailed version too.

Himanshi27 September 2020

Although the content is good!!! But can be improved in terms of more Hands-on exercises and also its presentation part can be improved further as its mostly reading from Slides.

Erick29 August 2020

The content of the course is good, but it is not enough to obtain the certification. Another practise exam would be great, also.

Raigon24 August 2020

could give more test practices need to cover more material of vision, speech, vision code example will be good apache beam example will be also good addition AI Platform example on training and prediction to be included AutoML example would be also good

Vamsidhar19 August 2020

This course is for beginners who are getting started with GCP for Data Engineering. Can have more clearer explaination with examples, people without computer science background will struggle a lot. The course covers maximum topics needed but not much in detail, poor video quality especially text from GCP console, this may affect users eyes a lot. I'm just exploring, this is my first course in the site just checking how it works and I'm not serious about certification or anything.

Andres3 August 2020

It is a very objective course and points out the key points of the services that google has available


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