A Professional Data Engineer enables data-driven decision making by collecting, transforming, and publishing data. A data engineer should be able to design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability. A Data Engineer should also be able to leverage, deploy, and continuously train pre-existing machine learning models.
The Professional Data Engineer exam assesses your ability to:
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Design data processing systems
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Build and operationalize data processing systems
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Operationalize machine learning models
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Ensure solution quality
The Professional Data Engineer Learning Path cover the following chapters:
1. Google Cloud Platform Big Data and Machine Learning Fundamentals (On-demand Training)
1.1 Google Cloud Platform Big Data and Machine Learning Fundamentals
2. Data Engineering on Google Cloud Platform (On-demand Training)
2.1 Google Cloud Platform Big Data and Machine Learning Fundamentals
2.2 Modernizing Data Lakes and Data Warehouses with GCP
2.3 Building Batch Data Pipelines on GCP
2.4 Building Resilient Streaming Analytics Systems on GCP
2.5 Smart Analytics, Machine Learning, and AI on GCP
3. Create and Manage Cloud Resources (Qwiklabs Quest)
3.1 A Tour of Google Cloud Hands-on Labs
3.2 Creating a Virtual Machine
3.2 Compute Engine: Qwik Start - Windows
3.3 Getting Started with Cloud Shell and gcloud
3.4 Kubernetes Engine: Qwik Start
3.5 Set Up Network and HTTP Load Balancers
3.6 Create and Manage Cloud Resources: Challenge Lab
4. Perform Foundational Data, ML, and AI Tasks in Google Cloud (Qwiklabs Quest)
4.1 AI Platform: Qwik Start
4.2 Dataprep: Qwik Start
4.3 Dataflow: Qwik Start - Templates
4.3 Dataflow: Qwik Start - Python
4.4 Dataproc: Qwik Start - Console
4.4 Dataproc: Qwik Start - Command Line
4.6 Cloud Natural Language API: Qwik Start
4.7 Google Cloud Speech API: Qwik Start
4.8 Video Intelligence: Qwik Start
4.9 Perform Foundational Data, ML, and AI Tasks in Google Cloud: Challenge Lab
5. Engineer Data in Google Cloud (Qwiklabs Quest)
5.1 Creating a Data Transformation Pipeline with Cloud Dataprep
5.2 Building an IoT Analytics Pipeline on Google Cloud
5.3 ETL Processing on Google Cloud Using Dataflow and BigQuery
5.4 Predict Visitor Purchases with a Classification Model in BQML
5.5 Cloud Composer: Copying BigQuery Tables Across Different Locations
5.6 Engineer Data in Google Cloud: Challenge Lab
6 Professional Data Engineer Exam (Certification)
6.1 Professional Data Engineer