Google Professional-Data-Engineer

Understanding functional and technical aspects of Google Professional Data Engineer Exam Designing data processing systems

The following will be discussed here:

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

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

Career Path

Completing the exam associated with the Google Professional Data Engineer certification provides you with a great validation of your skills in designing, building, operationalizing, securing, and monitoring data processing systems. The job roles that you can take up after getting certified include a Google Cloud Data Engineer, an Operations Engineer, a Cloud Infrastructure Engineer, a DevOps Infrastructure Engineer, a Cloud Database Engineer, a Google Cloud IAM Engineer, a DataOps Engineer, a Big Data Engineer, a Google Cloud Platform Data Architect, and more. The average salary that you can expect to earn with this certificate is around $125,550 per year. However, the real remuneration will depend on a specific job title, location of an individual, and his/her working experience.

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