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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:

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

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

The following will be discussed here:

Data acquisition and import
Provisioning resources
Batch and streaming
Monitoring pipelines
Building and operationalizing storage systems
Lifecycle management of data
Testing and quality control
Validating a migration
Effective use of managed services (Cloud Bigtable, Cloud Spanner, Cloud SQL, BigQuery, Cloud Storage, Cloud Datastore, Cloud Memorystore)
Building and operationalizing data processing systems
Migrating from on-premises to cloud (Data Transfer Service, Transfer Appliance, Cloud Networking)
Building and operationalizing pipelines
Integrating with new data sources
Adjusting pipelines
Transformation
Data cleansing
Storage costs and performance
Awareness of current state and how to migrate a design to a future state
Building and operationalizing processing infrastructure

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

Understanding functional and technical aspects of Google Professional Data Engineer Exam Ensuring solution quality

The following will be discussed here:

Legal compliance (e.g., Health Insurance Portability and Accountability Act (HIPAA), Children's Online Privacy Protection Act (COPPA), FedRAMP, General Data Protection Regulation (GDPR))
Verification and monitoring
Ensuring privacy (e.g., Data Loss Prevention API)
Pipeline monitoring (e.g., Stackdriver)
Ensuring reliability and fidelity
Ensuring scalability and efficiency
Data security (encryption, key management)
Building and running test suites
Designing for security and compliance
Ensuring flexibility and portability
Resizing and autoscaling resources
Designing for data and application portability (e.g., multi-cloud, data residency requirements)
Assessing, troubleshooting, and improving data representations and data processing infrastructure
Identity and access management (e.g.,Cloud IAM)
Choosing between ACID, idempotent, eventually consistent requirements
Mapping to current and future business requirements
Data staging, cataloging, and discovery
Planning, executing, and stress testing data recovery (fault tolerance, rerunning failed jobs, performing retrospective re-analysis)
Performing data preparation and quality control (e.g., Cloud Dataprep)

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