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Data Engineer – Professional Certification Preparation for Google

Intermediate

LP Box Courses 9 Video Courses
LP Box quiz 5 Quiz sessions
LP Box Lab No hands-on labs
Duration 7h 46m

Learning Path Description

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 seven of these courses include hands-on demos you can do yourself. Then you can test what you’ve learned by taking the five quizzes included. Finally, watch the “Additional Topics” video to find out what other resources you should review before taking the exam.

 

Learning Objectives

  • 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

Prerequisites

  • Basic database knowledge

Intended Audience

  • Data professionals
  • People studying for the Google Professional Data Engineer exam

This Learning Path Includes

  • 9 video courses
  • 5 quizzes

Feedback

If you have thoughts or suggestions for this learning path, please contact Cloud Academy at support@cloudacademy.com.

Updates

Apr. 9, 2018: Added Building Convolutional Neural Networks on Google Cloud course 

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1

BigQuery is Google’s managed data warehouse in the cloud. BigQuery is incredibly fast. It can scan billions of rows in seconds. It’s also surprisingly inexpensive and easy to use. Querying terabytes of data costs only pennies and you only pay for what you use since there are no up-front costs. This is a hands-on course where you can follow along with the demos using your own Google Cloud account ...

2

BigQuery is Google's incredibly fast, secure, and surprisingly inexpensive data warehouse, but there are ways to make it even faster, cheaper, and more secure. Here are some examples of what you will learn in this course: BigQuery can process billions of rows in seconds, but only if you break the rules of relational database design. If you are analyzing relatively small amounts of data, then your ...

3

Course Description Google Data Studio is a web-based application for creating reports and dashboards. It’s an easy-to-use tool for displaying your data visually. It was designed to help Google Analytics users create custom reports, but it can now read data from many sources, including BigQuery, Cloud SQL, and Cloud Storage. In this course, you will learn how to connect a Data Studio report to a ...

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Machine learning is a hot topic these days and Google has been one of the biggest newsmakers. Recently, Google’s AlphaGo program beat the world’s No. 1 ranked Go player. That’s impressive, but Google’s machine learning is being used behind the scenes every day by millions of people. When you search for an image on the web or use Google Translate on foreign language text or use voice dictation on ...

6

Course Description Once you know how to build and train neural networks using TensorFlow and Google Cloud Machine Learning Engine, what’s next? Before long, you’ll discover that prebuilt estimators and default configurations will only get you so far. To optimize your models, you may need to create your own estimators, try different techniques to reduce overfitting, and use custom clusters to ...

8

Most organizations are already gathering and analyzing big data or plan to do so in the near future. One common way to process huge datasets is to use Apache Hadoop or Spark. Google even has a managed service for hosting Hadoop and Spark. It’s called Cloud Dataproc. So why do they also offer a competing service called Cloud Dataflow? Well, Google probably has more experience processing big data ...

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Course Description Google Cloud Dataproc is a managed service for running Apache Hadoop and Spark jobs. It can be used for big data processing and machine learning. But you could run these data processing frameworks on Compute Engine instances, so what does Dataproc do for you? Dataproc actually uses Compute Engine instances under the hood, but it takes care of the management details for you. ...

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Bigtable is an internal Google database system that’s so revolutionary that it kickstarted the NoSQL industry. In the mid 2000’s, Google had a problem. The web indexes behind its search engine had become massive and it took a long time to keep rebuilding them. The company wanted to build a database that could deliver real-time access to petabytes of data. The result was Bigtable. Google went on ...

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This short video gives you a list of some of the other resources you should review before taking the Google Certified Professional Data Engineer exam.
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Data Engineer – Professional Certification Preparation for Google
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