Google Professional Data Engineer Certified Professional salary
The average salary of a Google Professional Data Engineer Certified Expert in
- Europe - 135,347 EURO
- England - 115,632 POUND
- United State - 151,247 USD
- India - 25,42,327 INR
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:
- Batch and streaming data (e.g., Cloud Dataflow, Cloud Dataproc, Apache Beam, Apache Spark and Hadoop ecosystem, Cloud Pub/Sub, Apache Kafka)
- At least once, in-order, and exactly once, etc., event processing
- Online (interactive) vs. batch predictions
- Designing data pipelines
- Architecture options (e.g., message brokers, message queues, middleware, service-oriented architecture, serverless functions)
- Mapping storage systems to business requirements
- Capacity planning
- Data publishing and visualization (e.g., BigQuery)
- Schema design
- Job automation and orchestration (e.g., Cloud Composer)
- Hybrid cloud and edge computing
- Selecting the appropriate storage technologies
- Distributed systems
- Designing data processing systems
- Choice of infrastructure
- Data modeling
- System availability and fault tolerance
- Tradeoffs involving latency, throughput, transactions
- Use of distributed systems
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Requirements
The certification does not have any official prerequisites. However, it is advised to have at least three years of industry experience with one or more years of expertise in designing and managing different solutions with the use of Google Cloud Platform. It is also required to review the topics of the qualifying exam before sitting for it.
Google Professional-Data-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
| Topic 1: Ensuring solution quality and reliability | 17% | - Testing and validating data systems
- 1. Performance and scalability testing
- 2. Data quality validation
- Troubleshooting and optimization
- 1. Optimizing queries and workloads
- 2. Diagnosing performance issues
|
| Topic 2: Operationalizing machine learning models | 20% | - Deploying and maintaining ML models
- 1. Optimizing model performance and cost
- 2. Model serving and monitoring
- Preparing data for ML
- 1. Feature engineering and data preparation
- 2. Handling structured and unstructured data
|
| Topic 3: Designing data processing systems | 20% | - Designing for regulatory and security requirements
- 1. Implementing access control and data protection
- 2. Ensuring data privacy and compliance
- Designing for business requirements
- 1. Selecting appropriate storage solutions
- 2. Designing for scalability and elasticity
- 3. Designing for reliability and fault tolerance
|
| Topic 4: Building and operationalizing data processing systems | 25% | - Deploying and managing systems
- 1. Monitoring and logging data processes
- 2. Managing infrastructure and resources
- Building data pipelines
- 1. Ingesting data from various sources
- 2. Orchestrating data workflows
- 3. Transforming and cleaning data
|
| Topic 5: Maintaining and automating data workloads | 18% | - Automation and repeatability
- 1. Automating deployment and updates
- 2. Implementing CI/CD for data systems
- Resource optimization
- 1. Choosing appropriate compute and storage options
- 2. Cost management and resource allocation
|