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The candidates must develop practical skills in the exam topics to succeed. These objectives are highlighted below:

Design Data Processing Systems

• Select the Relevant Storage Technologies: The considerations for this area include mapping storage systems to the business needs, data modeling, distributed systems, as well as tradeoffs, involving transactions, throughput, and latency;
• Design Data Processing Solutions: This topic includes the individuals’ expertise in planning, distributed systems usage, choice of infrastructure, hybrid Cloud & edge computing, system availability & fault tolerance. You should also know about the architecture options, including message queues, message brokers, service-oriented architecture, middleware, and serverless function;
• Design Data Pipeline: The focus for this subsection includes data visualization & publishing and batch & streaming data (Cloud Dataproc, Cloud Dataflow, Cloud Sub/Pub, Hadoop ecosystem, Apache Spark, Apache Beam, and Apache Kafka). It also focuses on online versus batch prediction and job orchestration & automation;
• Migrate Data Processing & Data Warehousing: This section includes validating migrations, migration from on-premises to Cloud, and awareness of the current state & how to migrate designs to the future state.

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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:

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

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

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This course is normally taken by data scientists, data analysts, and business analysts who are in the field of Google Сloud. It is a good way to prepare for your final exam because it teaches you all the details including 7 modules that cover all the Professional Data Engineer exam objectives:

• Data Processing Architectures
• Introducing Google Cloud Platform ‘
• Compute and Storage Fundamentals
• Additional Resources
• Scaling Data Analytics
• Machine Learning
• Data Analytics on the Cloud

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

The following will be discussed here:

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