Building Batch Data Pipelines on Google Cloud Course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
Overview (80-120 words) describing structure and time commitment.
Module 1: GCP Data Fundamentals
Estimated time: 10 hours
- Cloud Storage architectures
- BigQuery best practices
- Dataflow vs. Dataproc comparison
- IAM and security configurations
Module 2: Pipeline Development
Estimated time: 14 hours
- Dataflow SDK (Java/Python)
- SQL transformations in BigQuery
- Cloud Functions for event-driven workflows
- Terraform infrastructure-as-code
Module 3: Orchestration
Estimated time: 12 hours
- Cloud Composer setup
- DAG authoring for Airflow
- Error handling strategies
- Dependency management
Module 4: Optimization
Estimated time: 10 hours
- Partitioning and clustering
- Slot reservations
- Cost monitoring tools
- Performance benchmarking
Module 5: Monitoring and Troubleshooting
Estimated time: 8 hours
- Log analysis with Cloud Logging
- Pipeline failure diagnostics
- Alerting with Cloud Monitoring
Module 6: Final Project
Estimated time: 16 hours
- Design an end-to-end batch pipeline on GCP
- Implement ETL/ELT using Dataflow and BigQuery
- Deploy and monitor pipeline with Cloud Composer and Terraform
Prerequisites
- Familiarity with GCP core services
- Basic Python or Java programming
- Understanding of SQL and data modeling
What You'll Be Able to Do After
- Design and implement batch data processing systems
- Integrate Cloud Storage, BigQuery, and Cloud SQL
- Automate workflows with Cloud Composer (Apache Airflow)
- Implement ETL/ELT patterns at scale
- Monitor, optimize, and troubleshoot data pipelines