What you will learn in Building Batch Data Pipelines on Google Cloud Course
- Design and implement batch data processing systems
- Master Cloud Storage, BigQuery, and Cloud SQL integrations
- Automate workflows with Cloud Composer (Apache Airflow)
- Implement ETL/ELT patterns at scale
- Optimize pipeline performance and cost
- Monitor and troubleshoot data pipelines
Program Overview
GCP Data Fundamentals
⏱️ 2-3 weeks
- Cloud Storage architectures
- BigQuery best practices
- Dataflow vs. Dataproc comparison
- IAM and security configurations
Pipeline Development
⏱️3-4 weeks
- Dataflow SDK (Java/Python)
- SQL transformations in BigQuery
- Cloud Functions for event-driven workflows
- Terraform infrastructure-as-code
Orchestration
⏱️3-4 weeks
- Cloud Composer setup
- DAG authoring for Airflow
- Error handling strategies
- Dependency management
Optimization
⏱️2-3 weeks
- Partitioning and clustering
- Slot reservations
- Cost monitoring tools
- Performance benchmarking
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Job Outlook
- High-Demand Roles:
- GCP Data Engineer (110K180K)
- Cloud Solutions Architect (130K220K)
- ETL Developer (90K150K)
- Industry Trends:
- 65% of enterprises using GCP for data pipelines
- 40% year-over-year growth in cloud data roles
- Google Cloud certifications boost salaries by 15-25%
Specification: Building Batch Data Pipelines on Google Cloud
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