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

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

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The Google Professional Data Engineer certification is designed to equip the individuals with the required knowledge and skills to enable data-driven decision-making through collecting, transforming, and publishing data. To earn this certificate, the candidates will be required to pass a single test measuring their skills in leveraging, deploying, and continuously training pre-existing machine learning models. The qualifying exam also evaluates the ability of the applicants to design, build, operationalize, monitor, and secure data processing systems.

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