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Don't wait and enroll in these training courses offered by the official vendor that will help you ace the Professional Data Engineer exam with a good score. Once you take this exam and earn the prestigious Professional Data Engineer certification, you will get a chance to obtain a high-paying job and an amazing opportunity to work with the experts. Don't waste your time on other tasks and start preparing for this exam today. The more you practice the more you will get closer to success as a data analyst or data engineer. Moreover, it will polish your skills throughout and allow you to efficiently in well-reputed companies.

Data Engineering on Google Cloud course

It is a 4-day course that gives hands-on experience to the candidates and allows them to build data processing systems on Google Cloud. It will also show you how to design data processing systems, analyze data and build end-to-end data pipelines and machine learning. In order to get a better understanding of the course, you need to complete the big data machine learning course or get equivalent experience. This course also aids you in developing applications using a programming language such as Python and covers the following objective:

• Enable insights from streaming data
• Influencing unstructured data using ML APIs on Cloud Dataproc
• Designing and building data processing systems on the Google Cloud Platform
• Processing batch and streaming data by using autoscaling data pipelines on Cloud Dataflow
• Predicting machine models using TensorFlow and Cloud ML

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

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

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