Google Professional Data Engineer Study Roadmap

Domain 1: System Design — Designing data processing systems, batch vs streaming architectures

Domain 1: Storage Selection — Choosing between BigQuery, Cloud SQL, Spanner, Bigtable, Firestore

Domain 2: Ingestion Pipelines — Pub/Sub, Dataflow (Apache Beam), Dataproc (Spark/Hadoop), batch loading

Domain 2: Data Transformation — Dataflow transforms, Dataproc jobs, Cloud Data Fusion, ETL patterns

Domain 3: Cloud Storage — Storage classes, lifecycle policies, Cloud SQL, Spanner, Bigtable design

Domain 3: BigQuery Deep Dive — Partitioning, clustering, materialized views, slots, BI Engine

Domain 4: Analytics — BigQuery ML, Looker, Dataprep, Vertex AI integration, data visualization

Domain 4: Data Quality — Dataplex, data lineage, data profiling, catalog management

Domain 5: Pipeline Ops — Monitoring pipelines, Dataflow autoscaling, error handling, scheduling

Domain 5: Automation — Cloud Composer (Airflow), Cloud Scheduler, Workflows, CI/CD for data

Domain 5: Security — IAM for data, VPC-SC, column-level security, DLP API, encryption

Full Review: Practice exams, Hands-on labs, Weak areas, Exam logistics