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Build & Operationalize Data Processing Systems

• Build & Operationalize Pipeline: This module requires that the learners demonstrate competence in data cleansing, transformation, batch & streaming, data import & acquisition, as well as integration with the new data sources;
• Build & Operationalize Storage Systems: This part will require the students’ skills and competence in the effective usage of managed services, including Cloud Spanner, CLoug Bigtable, BigQuery, Cloud SQL, Cloud Memorystore, Cloud Datastore, and Cloud Storage. It also covers their skills in managing the data lifecycle and storage performance and costs;
• Build & Operationalize Processing Infrastructure: The considerations for this subject area include provisioning resources, adjusting pipeline, monitoring pipeline, and testing & quality control.

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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:

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

Google Professional-Data-Engineer Exam Syllabus Topics:

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

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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