Google Professional-Data-Engineer certification is a globally recognized certification that validates the skills and expertise of professionals in the field of data engineering. Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版) certification is offered by Google and is designed to test the proficiency of individuals in designing, building, and managing data processing systems, as well as analyzing and interpreting data. Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版) certification is aimed at individuals who are looking to enhance their career prospects in the field of data engineering.

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Google Professional-Data-Engineer (Google Certified Professional Data Engineer) Exam is a certification program that validates the skills of a data engineer in designing, building, and managing data processing systems. It is designed to assess the proficiency of a data engineer in using Google Cloud technologies to develop and deploy scalable, reliable, and efficient data pipelines. Professional-Data-Engineer-JPN exam covers various topics, including data processing architectures, data modeling, data analysis, and machine learning.

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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:

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

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

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