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Building & Operationalizing Data Processing Systems
Within this subject area, the test takers should show that they know how to build and operationalize storage systems. Specifically, they need to be conversant with effective use of managed services (such as Cloud Bigtable, Cloud SQL, Cloud Spanner, BigQuery, Cloud Storage, Cloud Memorystore, Cloud Datastore), storage costs & performance, and lifecycle management of data. The students should also be capable of building as well as operationalizing pipelines, including such technical tasks as data cleansing, transformation, batch & streaming, data acquisition & import, and integrating with new data sources. Apart from that, the candidates need to have sufficient competency to build and operationalize the processing infrastructure. This includes a good comprehension of provisioning resources, adjusting pipelines, monitoring pipelines, as well as testing & quality control.
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Reference: https://cloud.google.com/certification/data-engineer
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Who is the Professional Data Engineer Exam Intended for?
This exam is designed for individuals who are experts in designing, building, securing, and monitoring data processing systems with a particular emphasis on compliance and security. The candidate who wants to take the Professional Data Engineer exam should have the ability to deploy, leverage, and training pre-existing machine learning models. Moreover, every applicant should have experience of more than 3 years including 1-year experience in designing and handling solutions utilizing GCP.
Google Professional-Data-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|
| Ingesting and processing the data (~20% of the exam) | 20% | - Building and maintaining data structures and databases
- 1. Planning for analytical and operational use cases
- 2. Defining data lifecycle
- Deploying and operationalizing the pipelines
- 1. CI/CD for data pipelines
- 2. Job automation and orchestration (Cloud Composer, Workflows)
- Performing security considerations
- 1. Auditing, privacy, and compliance
- 2. Data encryption
- 3. Identity and Access Management (IAM)
|
| Maintaining and automating data workloads (~15% of the exam) | 15% | - Monitoring data pipelines and data processes
- 1. Logging, monitoring, and troubleshooting
- 2. Managing quotas and resource usage
- Automating data processes
- 1. Workflow orchestration
- 2. Scheduling jobs
- 3. Continuous integration and continuous deployment (CI/CD)
- Designing for reliability and fidelity
- 1. Recovering from failures
- 2. Planning for monitoring and alerting
- 3. Performing data quality and validation checks
|
| Preparing and using data for analysis (~15% of the exam) | 15% | - Sharing data securely
- 1. Publishing datasets
- 2. Data sharing and collaboration
- Preparing data for visualization
- 1. Connecting to Looker and other BI tools
- 2. Preparing data for reporting and dashboards
|
| Storing the data (~20% of the exam) | 20% | - Planning for using a data warehouse
- 1. Mapping business requirements
- 2. Deciding the degree of data normalization
- 3. Defining architecture to support data access patterns
- 4. Designing the data model
- Selecting storage systems
- 1. Lifecycle management of data
- 2. Analyzing data access patterns
- 3. Planning for storage costs and performance
- Using a data lake
- 1. Managing the lake (data discovery, access, cost controls)
- 2. Processing data
- 3. Monitoring the data lake
- Designing for a data platform
- 1. Building a data platform using Dataplex, Dataplex Catalog, BigQuery, Cloud Storage
- 2. Building a federated governance model for distributed data systems
|
| Designing data processing systems (~30% of the exam) | 30% | - Designing data pipelines
- 1. Data acquisition and import
- 2. Streaming (e.g., windowing, late arriving data)
- 3. Integrating with new data sources
- 4. Processing logic
- 5. Batch processing
- 6. AI data enrichment
- Selecting appropriate storage technologies
- 1. Mapping storage options to business requirements
- 2. Choosing between BigQuery, Bigtable, Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore, AlloyDB
- Designing data processing resources
- 1. Cluster sizing and autoscaling
- 2. Cost optimization
- 3. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run)
|
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