What will you learn in Preparing for Google Cloud Certification: Cloud Data Engineer Professional Certificate Course
Design, build, and maintain data processing systems on GCP, including BigQuery, Cloud Storage, Dataproc, and Pub/Sub.
Develop batch and streaming ETL pipelines and data warehouse solutions at scale.
Use machine learning tools and fundamentals to create ML-based analytics applications.
Optimize performance, security, and reliability of data systems in production environments.
Program Overview
Module 1: Big Data & Machine Learning Fundamentals
⏳ ~4 weeks (5 hr/week)
Topics: Core GCP data and ML services; big data architectures.
Hands-on: Qwiklabs on BigQuery, Cloud Storage, and machine learning pipelines.
Module 2: Modernizing Data Lakes and Warehouses
⏳ ~4 weeks
Topics: Data lake vs warehouse, ingestion strategies, management patterns.
Hands-on: ETL pipelines with Cloud Storage, BigQuery, Dataproc.
Module 3: Building Batch Data Pipelines
⏳ ~4 weeks
Topics: Orchestration with Dataflow, scheduling, error handling.
Hands-on: Build scalable batch pipelines in Qwiklabs.
Module 4: Streaming Analytics Systems
⏳ ~4 weeks
Topics: Real-time ingestion with Pub/Sub, Dataflow streaming, windowing.
Hands-on: Create live streaming ETL jobs.
Module 5: Smart Analytics, Machine Learning & AI
⏳ ~4 weeks
Topics: ML model deployment, inference pipelines, AI integration.
Hands-on: Setup ML workflows in Qwiklabs and use AI APIs.
Module 6: Preparing for the Professional Data Engineer Journey
⏳ ~4 weeks
Topics: Prepare study plans, exam domains, mock questions.
Hands-on: Diagnostic quizzes, create a personalized study plan.
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Job Outlook
This certificate prepares learners for roles such as Cloud Data Engineer, Data Architect, and ML Engineer on GCP.
Median completion time is ~3.5 months at 5 hours/week, aligning with industry expectations.
Graduates gain hands-on experience using real GCP services; professional Data Engineer roles are among the top‑paying cloud certifications.
Specification: Preparing for Google Cloud Certification: Cloud Data Engineer Professional Certificate
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FAQs
- Prior experience with SQL, ETL, and Python is recommended.
- Basic familiarity with GCP services improves lab efficiency.
- True beginners may find modules challenging but manageable with extra study.
- Prepares learners for the Google Professional Data Engineer exam.
- Builds confidence through structured hands-on labs and practice questions.
- Extensive labs using Qwiklabs for core GCP services.
- Practice designing batch and streaming ETL pipelines.
- Hands-on ML model deployment and AI API integration.
- Diagnostic quizzes and mock exams mimic certification scenarios.
- Reinforces both practical skills and exam readiness.
- Prepares for Cloud Data Engineer and Data Architect positions.
- Builds skills applicable for ML Engineer roles on GCP.
- Hands-on labs demonstrate enterprise-level data engineering workflows.
- Supports preparation for one of the top-paying cloud certifications.
- Enhances employability and portfolio with real-world GCP experience.
- No dedicated capstone; learning is through module labs.
- Includes diagnostic quizzes and mock exam questions.
- Personalized study plans guide exam readiness.
- Hands-on exercises reinforce theory and practical application.
- Encourages integration of skills across modules for real-world practice.
- Median completion time is ~3.5 months at 5 hours/week.
- Each module takes ~4 weeks with 5 hours of work weekly.
- Hands-on labs may extend study time depending on prior experience.
- Flexible pacing accommodates full-time work or other commitments.
- Completion provides both a certificate and exam readiness.
