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.

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

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