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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:

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

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Target Audience

The candidates for this certification are the data engineers or those aiming to become one. These individuals should have the capacity to allow data-driven decision-making through the collection, transformation, and publishing of data. They have the expertise in designing, building, and operationalizing secure data processing systems and monitoring the same. This is with the specific emphasis on compliance and security, fidelity and reliability, portability and flexibility, as well as efficiency and scalability.

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

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

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

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

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