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

The following will be discussed here:

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

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Introduction

Data engineers are responsible for finding trends in data sets and developing algorithms to help make raw data more useful to the enterprise. This IT role requires a significant set of technical skills, including a deep knowledge of SQL database design and multiple programming languages They collect, transform, and visualize data. The Data Engineer designs, builds, maintains, and troubleshoots data processing systems with a particular emphasis on the security, reliability, fault-tolerance,scalability, fidelity, and efficiency of such systems.

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

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Google Professional-Data-Engineer Exam Syllabus Topics:

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