Google Professional-Data-Engineer日本語 : Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版)

Who should take the Google Professional Data Engineer exam

Individuals should pursue the exam if they want to demonstrate their expertise and ability to design and develop Data Engineering. Following professional get benefited from Google Professional Data Engineer Certification

• Business analysts
• Data analysts
• Data engineers
• Data architects
• Developers responsible for managing big data transformation initiatives
• Data scientists

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Training Courses Recommended for the Exam Preparation

Training courses are meant to help candidates to learn about the Google exam syllabus and prepare well. It has hands-on labs and expert support that will allow you to get in-depth knowledge of each domain covered in the test. So, these are some of the best training courses offered by Google for the Professional Data Engineer certification exam.

The candidates must develop practical skills in the exam topics to succeed. These objectives are highlighted below:

Design Data Processing Systems

• Select the Relevant Storage Technologies: The considerations for this area include mapping storage systems to the business needs, data modeling, distributed systems, as well as tradeoffs, involving transactions, throughput, and latency;
• Migrate Data Processing & Data Warehousing: This section includes validating migrations, migration from on-premises to Cloud, and awareness of the current state & how to migrate designs to the future state.
• Design Data Processing Solutions: This topic includes the individuals’ expertise in planning, distributed systems usage, choice of infrastructure, hybrid Cloud & edge computing, system availability & fault tolerance. You should also know about the architecture options, including message queues, message brokers, service-oriented architecture, middleware, and serverless function;
• Design Data Pipeline: The focus for this subsection includes data visualization & publishing and batch & streaming data (Cloud Dataproc, Cloud Dataflow, Cloud Sub/Pub, Hadoop ecosystem, Apache Spark, Apache Beam, and Apache Kafka). It also focuses on online versus batch prediction and job orchestration & automation;

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Understanding functional and technical aspects of Google Professional Data Engineer Exam Designing data processing systems

The following will be discussed here:

• Designing data processing systems
• Capacity planning
• Mapping storage systems to business requirements
• Designing data pipelines
• Data modeling
• Selecting the appropriate storage technologies
• Distributed systems
• At least once, in-order, and exactly once, etc., event processing
• Hybrid cloud and edge computing
• Tradeoffs involving latency, throughput, transactions
• Job automation and orchestration (e.g., Cloud Composer)
• Data publishing and visualization (e.g., BigQuery)
• System availability and fault tolerance
• Use of distributed systems
• Online (interactive) vs. batch predictions
• Schema design
• Choice of infrastructure
• Batch and streaming data (e.g., Cloud Dataflow, Cloud Dataproc, Apache Beam, Apache Spark and Hadoop ecosystem, Cloud Pub/Sub, Apache Kafka)
• Architecture options (e.g., message brokers, message queues, middleware, service-oriented architecture, serverless functions)

Reference: https://cloud.google.com/certification/data-engineer

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Google Professional-Data-Engineer日本語 Exam Syllabus Topics: