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

The following will be discussed here:

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

Exam Overview

The Professional Data Engineer certification exam is a 2-hour test consisting of the multiple-choice and multiple-select questions. The students can take it in the English or Japanese languages. To register for and schedule the exam, you must pay the fee of $200. It is possible to sit for this test in an online proctored format at a remote location. You can also take it as an on-site proctored exam at a designated testing center.

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Build & Operationalize Data Processing Systems

• Build & Operationalize Storage Systems: This part will require the students’ skills and competence in the effective usage of managed services, including Cloud Spanner, CLoug Bigtable, BigQuery, Cloud SQL, Cloud Memorystore, Cloud Datastore, and Cloud Storage. It also covers their skills in managing the data lifecycle and storage performance and costs;
• Build & Operationalize Processing Infrastructure: The considerations for this subject area include provisioning resources, adjusting pipeline, monitoring pipeline, and testing & quality control.
• Build & Operationalize Pipeline: This module requires that the learners demonstrate competence in data cleansing, transformation, batch & streaming, data import & acquisition, as well as integration with the new data sources;

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;
• 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.
• 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;

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

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