4.39 (920 reviews)
☑ 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 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
Clustering Partitioned Tables
Loading Data into BigQuery
Migrating a Data Warehouse
Assessing the Current State of a Data Warehouse
Schema and Data Transfer
Reporting and Analysis
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
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
IAM Best Practices
Ensuring Privacy with Data Loss Prevention API
Encryption At Rest and In Motion
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
Gradient Descent and Backpropagation
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
GCP Professional Data Engineer Practice Exam
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.
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.
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
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
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.
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.
presentation graphics are too blurry even in full screen mode. Please refine these to be sharper at 720p.
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.
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.
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.
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.
The content of the course is good, but it is not enough to obtain the certification. Another practise exam would be great, also.
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
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.
It is a very objective course and points out the key points of the services that google has available