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Google Professional Data Engineer Certification Course

Google Professional Data Engineer Certification Course

Master Google Cloud data engineering: Data Pipeline Design, Big Data Processing, Data Storage, Machine Learning, and Security

Live interactive sessions led by Google-certified data engineers and cloud experts

Comprehensive certification support with focused exam preparation and advanced technical mentoring

Earn the prestigious Google Professional Data Engineer credential, recognized worldwide

Key Highlights

Live instructor-led sessions facilitated by experienced Google Cloud professionals

Practical projects and quizzes designed for direct application to enterprise-scale scenarios

Instruction covering all domains of the Professional Data Engineer certification

Access to specialized communities and cloud-focused technical forums

About Google Professional Data Engineer Course: Overview

This course provides structured learning in theoretical and practical aspects of data engineering on Google Cloud Platform (GCP). Participants will gain proficiency in ingesting, processing, analyzing, securing, and automating data workloads. The curriculum blends content on BigQuery, Cloud Storage, Dataflow, Dataproc, Pub/Sub, and Vertex AI, reinforced by practical labs and industry-aligned projects that mirror the Google Professional Data Engineer (PDE) certification blueprint.

What Will You Learn in This Program?

The curriculum is arranged as a series of modules to ensure comprehensive coverage of necessary skills for contemporary data engineering on GCP:

Live Interactive Training Modules

    • Designing Data Processing Systems : Architectural best practices, pipeline planning, and scalable cloud-native design principles
    • Building Batch and Streaming Data Pipelines : Construction of ETL/ELT workflows with Dataflow, Dataproc, and Cloud Composer; mastery of streaming with Pub/Sub and Dataflow
    • Big Data Storage and Warehousing : Strategic use of Cloud Storage, optimizing data warehouses with BigQuery partitioning, clustering, and performance tuning
    • Machine Learning Integration for Data Engineers : Implementation of ML model workflows with Vertex AI, AutoML, and seamless integration into data pipelines
    • Data Security and Compliance : Application of robust security with IAM roles, encryption, audit logging, policy-based governance, and architectural compliance
    • Monitoring, Automation & Optimization : Implementation of pipeline monitoring, cost management, workflow automation, and infrastructure optimization using Stackdriver and related tools
    • Metadata & Cataloging : Management of enterprise metadata, discoverability, and schema evolution with Data Catalog
    • Exam Preparation and Mock Assessments : Targeted review sessions, scenario practice, and simulated assessments to ensure exam readiness

    • Design and deployment of scalable, highly reliable data architectures leveraging Google Cloud
    • Creation and optimization of batch and streaming data pipelines for advanced analytics
    • Management of big data workflows with tools like Dataflow and Dataproc
    • Strategic use of BigQuery for warehousing and analytical performance improvement
    • Integration of machine learning solutions through Vertex AI and BigQuery ML
    • Implementation of cloud-native security frameworks and continuous compliance
    • Monitoring, troubleshooting, and endpoint automation of data workflows
    • Management and governance of metadata and schema with Google Cloud Data Catalog
    • Optimization of cost and resource allocation across cloud deployments

    • Organizations increasingly depend on Google Cloud for scalable big data and machine learning workloads. Certification demonstrates advanced capability in building resilient data ecosystems and translating raw data into business value. Technical mastery in this area is essential for implementing analytics, automation, and AI initiatives in the evolving cloud landscape.

      Day-to-Day Work of a Google Professional Data Engineer

    • Collaborate with solution architects and analytics teams to develop data-driven cloud solutions
    • Construct, validate, and maintain pipelines supporting batch and streaming analytics
    • Refine BigQuery datasets and optimize complex queries for analytics performance
    • Integrate scalable data engineering pipelines with ML workflows and Vertex AI
    • Ensure that all security controls, encryption, and compliance measures align with industry and organizational standards
    • Monitor pipelines for performance, reliability, and maintainability using advanced monitoring tools
    • Provide thorough documentation and guidance for architecture design and solution decisions

    • Job Roles Enabled by This Certification

      Google Cloud Data Engineer
      Cloud Data Pipeline Architect
      Big Data Engineer with Cloud ML Integration
      Data Analytics Engineer
      Cloud Security and Governance Specialist
      Machine Learning Infrastructure Engineer

      Core Competencies Covered

      Cloud Data Architecture | Data Processing Pipelines | Big Data Warehousing | Streaming Analytics Machine Learning on GCP | Security & Compliance | Monitoring & Optimization | Metadata Management | Exam Readiness

      Sample Projects Included

    • Construction of end-to-end batch and streaming pipelines with Dataflow, Pub/Sub, and BigQuery
    • Deployment of scalable data lake infrastructures leveraging Cloud Storage and BigQuery
    • Building machine learning training and deployment pipelines with Vertex AI integration
    • Security modeling and enforcement through advanced IAM and data encryption
    • Development of automated dashboards using Stackdriver and Cloud Monitoring
    • Building metadata management solutions with Data Catalog and schema evolution support
    • Capstone scenario delivering an enterprise-ready architecture as simulated for certification exam objectives

    Career Support Services

    Instructional workshops for technical documentation, participation in Google Cloud communities, and access to technical discussion groups Peer-based review sessions for solution architecture and design Regular engagement with industry best practices and cloud platform updates

    Google Professional Data Engineer Certification

    Certification demonstrates in-depth technical expertise in architecting, building, and managing scalable data solutions on Google Cloud Platform.

    Earning the Google Professional Data Engineer credential validates mastery of cloud-native data engineering, automation, machine learning integration, and secure solution delivery, all integral to contemporary analytic and AI environments.

FAQ

Content emphasizes hands-on use of BigQuery, Cloud Storage, Dataflow, Dataproc, Pub/Sub, Cloud Composer, Vertex AI, Data Catalog, and foundational tools for security and monitoring.

Program content benefits data engineers, cloud infrastructure specialists, analytics professionals, and machine learning engineers targeting advanced skills in Google Cloud environments.

Familiarity can aid learning, but the curriculum starts with foundational principles, progressing to advanced applications with laboratory support for all backgrounds.

The full program mirrors the official exam framework, combining instructional content, hands-on scenario labs, mock assessments, and strategy sessions.

The design balances live instructor guidance, self-paced modules, practical labs, and technical collaboration for flexible and effective learning progression.

Earning the credential establishes technical authority in leveraging Google Cloud for complex data engineering demands, encompassing architecture, machine learning, and governance.
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