Build & Operationalize Data Processing Systems

• 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.
• 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;

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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:

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

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

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