This Learning Path is designed to help you prepare for the Google Certified Professional Data Engineer Exam. Even if you don't plan to take the Exam, these courses will help you gain a solid understanding of the various data processing components of the Google Cloud Platform.
At the heart of Google’s big data services is BigQuery, a managed data warehouse in the cloud. The first three courses will show you how to load and query data in BigQuery, optimize BigQuery’s performance, and visualize your data.
The next three courses will show you how to process your data. First, you will use Cloud Machine Learning Engine to train neural networks to perform predictive analytics. Next, you’ll use Cloud Dataflow and Cloud Dataproc to build data processing pipelines that transform and summarize your data using Apache Beam, Hadoop, and Spark.
The final course will introduce you to Bigtable, Google’s revolutionary NoSQL database. It will show you how to take advantage of Bigtable’s high performance for big data applications.
All of these courses include hands-on demos you can do yourself. Then you can test what you’ve learned by taking the practice exam.
- Design a data processing system
- Build and maintain data structures and databases
- Analyze data and enable machine learning
- Optimize data representations, data infrastructure performance, and cost
- Ensure reliability of data processing infrastructure
- Visualize data
- Design secure data processing systems
- Basic database knowledge
- Data professionals
- People studying for the Google Professional Data Engineer exam
If you have thoughts or suggestions for this Learning Path, please contact Cloud Academy at email@example.com.
- Aug. 1, 2019 - Added 4 courses and 1 lab:
- Google Cloud Platform: Fundamentals course
- Google Cloud Platform: Systems Operations course
- Designing a Google Cloud Infrastructure course
- Managing Your Google Cloud Infrastructure course
- Granting Access to Google Cloud Storage Objects with Signed URLs lab
- Apr. 9, 2018 - Added "Building Convolutional Neural Networks on Google Cloud" course
Learning Path Steps
This course introduces you to the fundamentals of Google Cloud Platform, including App Engine, Kubernetes Engine, Compute Engine, storage, BigQuery, Cloud Firestore, and app deployment.
This course covers Google Cloud systems operations, providing insight and practical information across the complete set of GCP features.
This course uses a case study to show how to apply the design principles of security, compliance, disaster recover to meet real-world requirements.
Granting Access to Google Cloud Storage Objects with Signed URLs
Use the gcloud CLI in Google Cloud Shell to create signed URLs to grant anyone access to objects stored in Google Cloud Storage for a set duration in this Lab.
This hands-on tutorial teaches you monitoring, testing, managing, and troubleshooting your GCP app infrastructure.
In this lab, you will create two tables in a SQL PostgreSQL database, perform operations on them, monitor the resources usage and test that the atomicity property is respected by the database.
Learn how to load data into BigQuery using files, run queries using standard SQL, and export data from BigQuery with this hands-on course.
This Lab will show you the basic concepts of BigQuery and will allow you to handle data and query them in a real GCP environment.
Learn how to make BigQuery faster, cheaper, and more secure with this hands-on course.
With this course, you'll learn how to visualize BigQuery Data with Google Data Studio and create BigQuery reports.
In this course, you'll learn how to train and deploy neural networks with Google Cloud Machine Learning Engine.
Learn how to build a CNN, train it on Machine Learning Engine and visualize its performance. Learn how to recognize overfitting and apply different methods to avoid it.
In this course, you'll learn how to write data processing programs using Apache Beam and run them using Cloud Dataflow, as well as learning how to run both batch and streaming jobs.
In this course, you'll learn how to run Hadoop and Spark jobs on GCP.
In this course, you'll learn which of your applications could make use of Bigtable and how to take advantage of its high performance.
This short video lists some of the other resources you should review before taking the Google Certified Professional Data Engineer exam.
GCP Data Engineer Certification Preparation
About the Author
Guy launched his first training website in 1995 and he's been helping people learn IT technologies ever since. He has been a sysadmin, instructor, sales engineer, IT manager, and entrepreneur. In his most recent venture, he founded and led a cloud-based training infrastructure company that provided virtual labs for some of the largest software vendors in the world. Guy’s passion is making complex technology easy to understand. His activities outside of work have included riding an elephant and skydiving (although not at the same time).