About This Study Plan
This 7-day study plan breaks the PDE (Data Engineer) exam preparation into 7 focused study sessions with 28 actionable tasks. The plan covers all 5 exam domains — Designing Data Processing Systems, Ingesting and Processing Data, Storing and Managing Data, Preparing Data for Analysis, Automating Data Workloads — ensuring complete coverage. Intensive 7-day review for the Google Professional Data Engineer exam covering data pipelines, storage, processing, ML, and security.
Prerequisites
- GCP data services experience
- SQL and Python proficiency
- 5–7 hours per day
Study Schedule
- BigQuery: architecture, partitioning, clustering, and materialized views
- Cloud SQL vs Spanner vs AlloyDB: when to use each
- Bigtable: schema design, row key patterns, and performance
- Cloud Storage: lifecycle rules, classes, and data lake patterns
- Dataflow (Apache Beam): batch and streaming pipelines
- Dataproc: managed Spark/Hadoop for ETL and analytics
- Pub/Sub: ingestion patterns, ordering, and exactly-once
- Dataflow vs Dataproc: decision criteria
- ETL/ELT patterns and data pipeline architecture
- Cloud Composer (Airflow): DAGs, scheduling, and dependencies
- Data Fusion for visual ETL and CDC patterns
- Streaming vs batch: windowing, watermarks, and triggers
- BigQuery ML: model types and when to use BQML vs Vertex AI
- Looker and Looker Studio for BI and visualization
- Vertex AI integration with data pipelines
- Feature Store and training data preparation
- Data governance: Data Catalog, DLP, and lineage
- Encryption: at rest, in transit, CMEK, and column-level
- IAM for data services and VPC Service Controls
- Monitoring data pipelines: logging, metrics, and alerting
- Take a full practice exam
- Review all incorrect answers
- Focus on BigQuery and Dataflow scenarios
- Review storage selection questions
- Data service selection flowchart
- BigQuery optimization cheat sheet
- Streaming patterns reference
- Rest before exam
Study Tips
BigQuery is the most tested service — know partitioning, clustering, and cost control.
Understand when to use Dataflow vs Dataproc vs BigQuery for processing.
Streaming pipeline design with windowing is heavily tested.
Recommended Google Cloud Study Resources
Supplement this study plan with Google Cloud Skills Boost, which provides structured learning paths aligned to each certification. The Google Cloud documentation is exceptionally detailed and frequently referenced in exam questions. Take advantage of the free $300 credit for new GCP accounts to build real projects during your study period.
Ready to Practice?
Put your study plan into action with Data Engineer practice questions.