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