Professional Data Engineer Training
This 4-day advanced training prepares data engineers to design, build, and manage data processing systems on Google Cloud. Participants gain practical experience with BigQuery, Dataflow, Pub/Sub, Cloud Composer, Dataproc, Vertex AI integration, and data governance tools. The course emphasizes both batch and streaming architectures with real-world design patterns.
Master data engineering on Google Cloud with this comprehensive 5-day training. Learn to design, build, and operationalize data processing systems, ensuring reliability, security, and scalability of data solutions.
Training Details
| Duration | 5 days (40 hours) |
| Level | Advanced |
| Delivery | In-person, Live online, Hybrid |
| Certification | Google Cloud Certified: Professional Data Engineer |
Who Is This For?
- Data engineers building data pipelines
- Analytics engineers working with big data
- ML engineers preparing data
- Anyone preparing for Professional Data Engineer certification
Learning Outcomes
After completing this training, participants will be able to:
- Design data processing systems
- Build and operationalize data pipelines
- Operationalize machine learning models
- Ensure solution quality and reliability
- Implement security and compliance for data
- Optimize costs of data solutions
Detailed Agenda
Day 1: Data Engineering Fundamentals
Module 1: Data Engineering on GCP
- Data engineering lifecycle
- GCP data services overview
- Data governance and compliance
- Hands-on: Plan data architecture
Module 2: BigQuery Fundamentals
- BigQuery architecture and storage
- SQL and query optimization
- Partitioning and clustering
- Hands-on: Build BigQuery datasets
Module 3: Data Loading and Export
- Batch loading strategies
- Streaming with BigQuery API
- Data Transfer Service
- Hands-on: Load data into BigQuery
Day 2: Batch and Stream Processing
Module 4: Dataflow for Batch Processing
- Apache Beam programming model
- Dataflow pipelines
- Transforms and windowing
- Hands-on: Build batch pipeline
Module 5: Streaming Data Processing
- Pub/Sub architecture
- Dataflow streaming
- Real-time analytics
- Hands-on: Build streaming pipeline
Module 6: Data Storage Options
- Cloud Storage for data lakes
- Cloud SQL and Cloud Spanner
- Bigtable for time-series data
- Hands-on: Choose storage solution
Day 3: Machine Learning and Advanced Analytics
Module 7: BigQuery ML
- Creating ML models in BigQuery
- Model evaluation and prediction
- Feature engineering
- Hands-on: Build ML model with BQML
Module 8: Vertex AI
- AutoML and custom training
- Model deployment and monitoring
- ML pipelines
- Hands-on: Deploy ML model
Module 9: Data Analysis and Visualization
- Looker and Data Studio
- Jupyter notebooks on Vertex AI
- Interactive analysis
- Hands-on: Create dashboards
Day 4: Data Quality and Security
Module 10: Data Quality and Validation
- Data quality patterns
- Data validation with Great Expectations
- Monitoring data pipelines
- Hands-on: Implement data quality checks
Module 11: Security and Privacy
- Data encryption strategies
- Column-level security in BigQuery
- Data Loss Prevention API
- Hands-on: Implement data security
Module 12: Compliance and Governance
- Data Catalog for metadata
- Policy tags and access controls
- Audit logging
- Hands-on: Implement governance
Day 5: Optimization and Operations
Module 13: Performance Optimization
- BigQuery query optimization
- Dataflow pipeline tuning
- Cost optimization strategies
- Hands-on: Optimize performance
Module 14: Operations and Monitoring
- Cloud Monitoring for data pipelines
- Logging and error handling
- Alerting strategies
- Hands-on: Monitor pipelines
Module 15: Exam Preparation
- Exam format and case studies
- Data engineering scenarios
- Practice questions
Prerequisites
- 2+ years data engineering experience
- SQL and programming knowledge (Python or Java)
- Understanding of data processing concepts
- GCP fundamentals
Delivery Formats
| Format | Description |
|---|---|
| In-Person | On-site at your company's location, hands-on with direct interaction |
| Live Online | Interactive virtual sessions with screen sharing and real-time labs |
| Hybrid | Combination of on-site and remote sessions, flexible scheduling |
All formats include hands-on labs, course materials, practice exams, and post-training support.
Ready to get started?
Request a training quote for your team — in-person, live-online, or hybrid.