Professional Data Engineer Training

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.