Google Professional-Data-Engineer Value Pack (Frequently Bought Together)

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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:

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

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Career Opportunities

The certified individuals can explore a variety of job opportunities. Some of the positions that they can take up include a Software Engineer, a Cloud Architect, a Data Engineer, a Sales Engineer, a Data Scientist, a Cloud Developer, and a Kubernetes Architect, among others. The salary outlook for these job roles is an average of $128,500 per annum.

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

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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:

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

The Professional Data Engineer exam is the industry-standard exam that proves the candidate’s ability to do data-driven decision-making by assembling, transforming, and publishing data. If you are rooting for a career in data engineering, you should take this test. It will lead you to attain the Professional Data Engineer certification issued by Google.

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