Preparing for Google Cloud Certification: Cloud Data Engineer Professional Certificate Course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
An intensive, lab-rich Professional Certificate that equips learners for the Google Data Engineer role—with strong exam alignment and real GCP experience. This course spans approximately 24 weeks with a recommended commitment of 5 hours per week, combining theoretical knowledge with hands-on Qwiklabs practice across core Google Cloud data services.
Module 1: Big Data & Machine Learning Fundamentals
Estimated time: 20 hours
- Core GCP data services and architecture
- Introduction to Google Cloud Machine Learning
- BigQuery for data analysis
- Cloud Storage integration and use cases
- Building ML pipelines on Google Cloud
Module 2: Modernizing Data Lakes and Warehouses
Estimated time: 20 hours
- Differences between data lakes and data warehouses
- Data ingestion strategies using Cloud Storage
- Data management patterns with BigQuery
- ETL workflows using Dataproc
Module 3: Building Batch Data Pipelines
Estimated time: 20 hours
- Dataflow for batch processing
- Orchestrating batch pipelines
- Scheduling data jobs
- Error handling and pipeline monitoring
Module 4: Streaming Analytics Systems
Estimated time: 20 hours
- Real-time data ingestion with Pub/Sub
- Streaming ETL pipelines using Dataflow
- Windowing and triggers in streaming data
Module 5: Smart Analytics, Machine Learning & AI
Estimated time: 20 hours
- Deploying ML models in production
- Building inference pipelines
- Integrating AI APIs into data workflows
Module 6: Preparing for the Professional Data Engineer Journey
Estimated time: 20 hours
- Review of exam domains and objectives
- Diagnostic quizzes and performance feedback
- Creating a personalized study plan
Prerequisites
- Familiarity with SQL
- Basic understanding of ETL processes
- Programming experience in Python
What You'll Be Able to Do After
- Design and build scalable data processing systems on GCP
- Develop both batch and streaming ETL pipelines
- Implement data warehouse and data lake solutions using BigQuery and Cloud Storage
- Integrate machine learning into analytics applications
- Optimize data systems for performance, security, and reliability