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GCP-PDE: Professional Data Engineer
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Find out more about GCP-PDE: Professional Data Engineer
The GCP Professional Data Engineer (PDE) course prepares learners to design, build, operationalize, secure, and monitor data processing systems on Google Cloud. It covers data ingestion, storage, transformation, and analytics using services like BigQuery, Dataflow, Pub/Sub, and Dataproc. The course emphasizes building scalable, reliable pipelines, applying machine learning models, and ensuring data governance, security, and compliance. Learners gain hands-on experience architecting end-to-end data solutions, optimizing performance, and supporting business decision-making through advanced analytics and AI-driven insights.
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About GCP-PDE: Professional Data Engineer
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Learn to design and manage scalable data processing systems effectively.
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Master data ingestion pipelines using Pub/Sub and Dataflow services.
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Implement batch and streaming analytics with BigQuery and Dataproc.
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Apply machine learning models with Vertex AI for predictions.
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Ensure strong data security, encryption, and role-based access control.
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Monitor pipelines, troubleshoot issues, and optimize overall system performance.
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Architect cloud-native solutions supporting real-time and batch processing workloads.
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Manage structured, unstructured, and semi-structured data across environments.
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Apply governance principles ensuring compliance with organizational and regulatory requirements.
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Gain hands-on labs experience solving real-world business data challenges.
Eligibility of the course
- No formal prerequisites – anyone can register for the exam.
- Recommended experience: At least 3+ years of industry experience.
- Google Cloud exposure: 1+ year designing and managing data solutions on GCP.
- Knowledge areas: Databases, data pipelines, analytics, and machine learning basics.
- Skills required: SQL, data modeling, ETL, and data warehousing concepts.
- Programming: Familiarity with Python, Java, or SQL for data tasks.
- Cloud concepts: Understanding of IAM, networking, and security best practices.
- Mindset: Ability to translate business requirements into data-driven solutions.
Key takeaways
- Comprehensive Study material
- Access to Practice tests
- Access to LMS course content
- Access to course assignments
- Industry-relevant case-studies
FAQs
Asked Questions & Answer
It validates skills in designing, building, and managing data solutions on Google Cloud.
No formal prerequisites, but Google recommends 3+ years of experience.
The exam is 2 hours, multiple-choice and multiple-select questions.
Data ingestion, storage, processing, analytics, machine learning, security, and governance.
The certification is valid for two years.
Use Google Cloud training, hands-on labs, practice exams, and case studies.
Strong knowledge of data modeling, SQL, ETL, ML, and GCP services.
Yes, familiarity with SQL and a language like Python or Java helps.
Data engineers, cloud architects, ML engineers, and analytics professionals.
Renew by retaking the exam before it expires in two years.
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