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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:

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

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

Certification Path

The Google Professional Data Engineer Certification is one of the highest level of certification mainly focussing to the professional Data Engineering.

There is no prerequisite for this exam but still it would be best to follow some sequence in order to prove immense knowledge as a Google professional Data Engineer.

You can complete Google Associate Certifications then approach for the professional certification. For more information related to Google cloud certification track Google-certification-path

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