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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:

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

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

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• Utilize Cloud SQL & Dataproc to migrate existing MySQL, Pig, Spark, or Hive workloads to Google Cloud
• Hire BigQuery and Cloud SQL for interactive data analysis

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