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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. 

Duration
35 hours
Fees
$399.00
Goal
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About GCP-PDE: Professional Data Engineer

  • Learn to design and manage scalable data processing systems effectively.

  • Master data ingestion pipelines using Pub/Sub and Dataflow services.

  • Implement batch and streaming analytics with BigQuery and Dataproc.

  • Apply machine learning models with Vertex AI for predictions.

  • Ensure strong data security, encryption, and role-based access control.

  • Monitor pipelines, troubleshoot issues, and optimize overall system performance.

  • Architect cloud-native solutions supporting real-time and batch processing workloads.

  • Manage structured, unstructured, and semi-structured data across environments.

  • Apply governance principles ensuring compliance with organizational and regulatory requirements.

  • Gain hands-on labs experience solving real-world business data challenges.

GCP-PDE: Professional Data Engineer Packages

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GCP-PDE Training

Comprehensive Coverage
Hands-On Labs
Practical Tools
Focus on Security
Exam-Oriented
Flexible Learning
Job-Ready Skills
Updated Content
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BEST SELLER

GCP-PDE Corporate Package

Comprehensive Coverage
Hands-On Labs
Practical Tools
Focus on Security
Exam-Oriented
Flexible Learning
Job-Ready Skills
Updated Content
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Course Contents

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  • Role of a data engineer
  • Overview of Google Cloud ecosystem
  • Data engineering workflows and use cases
  • Cloud Storage basics and best practices
  • Relational storage with Cloud SQL and AlloyDB
  • NoSQL storage with Firestore and Bigtable
  • Data warehouse with BigQuery
  • Real-time data streaming with Pub/Sub
  • ETL/ELT pipelines using Dataflow
  • Batch processing with Dataproc (Hadoop, Spark)
  • Data Transfer Service and Storage Transfer
  • Designing batch and streaming pipelines
  • Data modeling and schema design
  • Using Dataform and Dataprep for transformations
  • Optimizing queries and processing costs
  • Advanced analytics with BigQuery
  • Partitioning, clustering, and optimization strategies
  • BI integration with Looker Studio
  • Exploratory data analysis with notebooks
  • Overview of Vertex AI services
  • Training and deploying ML models
  • Integrating TensorFlow with BigQuery ML
  • MLOps and automated pipelines
  • Identity and Access Management (IAM)
  • Data encryption, key management, and audit logging
  • Policies for compliance and regulatory requirements
  • Data quality, lineage, and cataloging with Data Catalog
  • Cloud Monitoring and Cloud Logging
  • Troubleshooting pipelines and workflows
  • Scaling and cost optimization strategies
  • Best practices for reliability and availability
  • Building real-time analytics pipelines
  • Designing an end-to-end data lake and warehouse
  • Deploying ML models for business use cases
  • Simulated exam questions and scenarios

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


Course features

Comprehensive Coverage

Hands-On Labs

Practical Tools

Focus on Security

Exam-Oriented

Flexible Learning

Job-Ready Skills

Updated Content

Course Benefits

 Boosts opportunities in cloud data engineering and analytics roles. 

 Validates skills with Google Cloud’s professional-level credential. 

 Mastery of BigQuery, Dataflow, Pub/Sub, Vertex AI, and more. 

 Practical labs and projects build real-world problem-solving confidence. 

 Learn to design secure, compliant, and governed data solutions. 

 Opens doors to roles like Data Engineer, ML Engineer, Cloud Architect. 

 Ability to translate business requirements into data-driven solutions. 

 Learn optimized pipeline design to reduce costs and increase performance.

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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