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, you’ll 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.