By 2026, I will obtain the Google Professional Data Engineer certification. My core knowledge system revolves around the entire GCP data engineering process, covering five official fields: system design, pipeline construction, data management, analysis preparation, and workflow operation and maintenance. It is suitable for large-scale data, real-time stream processing, AI/ML integration, and high compliance requirements of European and American enterprises.

The following is the system architecture of Google Professional Data Engineer:

1. Design a data processing system

The core is to design a scalable and highly available end-to-end architecture based on business requirements, suitable for batch stream mixing and multi-source integration scenarios.

Architecture selection: distinguish between batch processing, stream processing, micro batch, and event driven architectures; Adapt to hybrid/multi cloud data access and evaluate serverless vs cluster solutions.

Data pipeline design: ETL/ELT process planning, Apache Beam unified programming model application, data collection and integration with new data sources, AI data augmentation.

Distributed systems and fault tolerance: ensuring exactly once/in order semantic processing, designing fault transfer and redundancy mechanisms, capacity planning to adapt to data growth, reducing latency and resource bottlenecks.

New focus for 2026: AI driven pipeline design, hybrid cloud data interconnection, and optimization of low latency stream processing architecture.

2. Build and operate a data processing system (20% -25%)

Focusing on the implementation pipeline of GCP services, covering storage selection, pipeline development and operation, and meeting the DevOps and cost optimization needs of European and American enterprises.

Data storage management: Select storage based on structured/semi-structured/unstructured data; Configure storage redundancy, tiered access, and lifecycle rules to optimize cost and performance.

Data pipeline development: Build a batch/stream unified pipeline using Dataflow, manage Spark/Hadoop clusters with Datapro, and integrate low code with Data Fusion; Pub/Sub processes real-time messages, Cloud Functions achieves serverless triggering; Process data conversion, cleaning, deduplication, and solve the problem of delayed data and window calculation.

Pipeline deployment and operation: containerization and CI/CD delivery, Cloud Composer orchestration of DAGs and scheduling, error handling, retry mechanism and dead letter queue design, version control and rollback strategy.

New focus for 2026: Dataplex data governance, BigLake cross source queries, and Dataflow flow flow batch optimization.

3. Design and Operations Data Governance and Security

Adapt to European and American compliance requirements such as GDPR, HIPAA, PCI, etc., ensure data security, quality, and governance, and meet the needs of enterprise data asset management.

Data governance system: Build a federated governance model using Dataplex, manage metadata with Dataplex Catalog, classify data and trace blood relationships; Design a data warehouse model to map business requirements and access patterns.

Security and Compliance: Minimize IAM roles and permissions, encrypt data during static/transmission, desensitize sensitive data through Cloud DLP, audit logs and access auditing; Implement row/column level security, data masking, and meet data localization and privacy compliance.

Data quality assurance: Design data validation rules, handle duplicate/missing/abnormal data, establish quality indicators and monitoring alarms, and ensure data consistency and accuracy.

New focus for 2026: AI data privacy protection, privacy compliance in RAG scenarios, cross regional data governance and auditing.

4. Prepare and use data for analysis

Supporting data analysis and AI/ML scenarios, covering data preparation, visualization, and sharing, adapting to the decision-making and AI driven needs of European and American enterprises.

Data preparation and visualization: cleaning, transformation, and feature engineering, supporting BI tool integration; prepare training data using BigQuery ML/Vertex AI, process unstructured data to generate embeddings for RAG.

Data sharing and collaboration: Publish datasets through BigQuery Analytics Hub, configure data sharing rules and permissions, generate reusable analysis reports and visual content.

New focus in 2026: AI assisted data preparation, embedded generation and vector database integration, and business value transformation of analysis results.

5. Maintain and automate data workloads

By automating and monitoring the system to ensure reliability, optimizing costs and performance, and adapting to the SLA and operational efficiency requirements of European and American enterprises.

Resource optimization: Balancing cost and performance, choosing persistent/job based clusters, reserving capacity and optimizing versions for BigQuery, reducing costs through storage tiering and lifecycle.

Automation and orchestration: Create DAGs with Cloud Composer, schedule and orchestrate batch/stream jobs, and achieve pipeline repeatability and CI/CD; Use Cloud Functions to respond to event triggered tasks.

Monitoring and troubleshooting: Cloud Monitoring/Logging configuration indicators and log queries, BigQuery management panel monitoring jobs, troubleshooting errors, quota and billing issues, establishing fault warning and recovery mechanisms.

New focus in 2026: AI anomaly detection, automatic scaling optimization, fault self-healing, and SLO guarantee.

6. Core Tools and 2026 Enhancement Direction

Core tool stack: Dataflow, Pub/Sub, Dataproc, Cloud Storage, Cloud Composer, Dataplex, Vertex AI, Cloud DLP, and IAM.
Essential skills: SQL, Apache Beam programming (Python/Java), data modeling, IAM and compliance design, pipeline orchestration and monitoring.

2026 Enhancement Direction: AI Data Enhancement and RAG Integration, Dataplex Federated Governance, BigLake Cross Source Analysis, Flow Processing Low Latency Optimization, Cost and Performance Refinement Management.

Summary: The system is centered around GCP hosting services, connecting the entire chain of "design build governance analysis operation", emphasizing architecture decision-making, pipeline reliability, security compliance, and AI integration, fully matching the data-driven and compliance priority needs of European and American enterprises. 

Preparing for the exam requires a combination of official learning paths and practical experience with GCP free quotas, with a focus on strengthening scenario based architecture design and troubleshooting capabilities.