Cloud Computing · PPL

Google Cloud Cloud Data Engineer Certification

Learn to design, build, secure, and optimize scalable data processing solutions using Google Cloud's modern data engineering services.

  • 4 DaysDuration
  • PPLAccredited
  • 3 LanguagesArabic · English · Hindi
  • ₹12,999.00 Per delegate

This course is accredited by PPL

This is for all ppl accredited courses
2M+ Delegates trained worldwide
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4.8 ★ Average learner rating
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— The journey

Course Outline

What the programme covers, module by module.

Module 1: Google Cloud Data Engineering Fundamentals

  • Google Cloud platform overview
  • Data engineering lifecycle
  • Google Cloud architecture
  • Core data services
  • Solution design principles

Module 2: Google Cloud Storage

  • Cloud Storage fundamentals
  • Storage classes
  • Object lifecycle management
  • Data organization
  • Data security

Module 3: BigQuery Fundamentals

  • BigQuery architecture
  • Datasets and tables
  • SQL querying
  • Data loading
  • Query optimization

Module 4: Data Ingestion Services

  • Batch data ingestion
  • Streaming data ingestion
  • Pub/Sub fundamentals
  • Data transfer services
  • Data import strategies

Module 5: Data Processing with Dataflow

  • Apache Beam concepts
  • Dataflow architecture
  • Batch pipelines
  • Streaming pipelines
  • Pipeline monitoring

Module 6: Data Transformation

  • ETL concepts
  • ELT workflows
  • Data cleansing
  • Data enrichment
  • Transformation best practices

Module 7: Cloud Dataproc

  • Managed Spark clusters
  • Hadoop ecosystem
  • Job execution
  • Cluster management
  • Performance tuning

Module 8: Cloud Composer

  • Workflow orchestration
  • Apache Airflow basics
  • DAG creation
  • Scheduling pipelines
  • Workflow monitoring

Module 9: Cloud SQL and Databases

  • Cloud SQL
  • Cloud Spanner overview
  • Firestore
  • Bigtable
  • Database selection

Module 10: Data Warehousing

  • Enterprise data warehouse concepts
  • BigQuery optimization
  • Partitioning
  • Clustering
  • Data modeling

Module 11: Streaming Analytics

  • Pub/Sub messaging
  • Real-time processing
  • Event-driven architecture
  • Streaming pipelines
  • Monitoring streams

Module 12: Data Security

  • Identity and Access Management
  • Data encryption
  • Cloud Key Management
  • Access controls
  • Security best practices

Module 13: Monitoring and Logging

  • Cloud Monitoring
  • Cloud Logging
  • Performance metrics
  • Alerting
  • Operational dashboards

Module 14: Machine Learning Integration

  • Vertex AI overview
  • BigQuery ML
  • Feature engineering
  • Data preparation
  • ML workflows

Module 15: Data Governance

  • Data quality
  • Metadata management
  • Data catalog
  • Governance policies
  • Lifecycle management

Module 16: Performance Optimization

  • Query optimization
  • Pipeline optimization
  • Resource management
  • Cost optimization
  • Performance monitoring

Module 17: Hybrid and Multi-Cloud Data Solutions

  • Hybrid data architecture
  • Data connectivity
  • Multi-cloud integration
  • Data migration
  • Cross-platform strategies

Module 18: Automation and Infrastructure

  • Infrastructure as Code
  • Deployment automation
  • Resource provisioning
  • CI/CD concepts
  • Operational automation

Module 19: Enterprise Data Architecture

  • Scalable architecture
  • High availability
  • Disaster recovery
  • Data resilience
  • Enterprise best practices

Module 20: End-to-End Data Pipeline Design

  • Data pipeline planning
  • Data ingestion
  • Transformation workflows
  • Analytics delivery
  • Performance evaluation

Module 21: Data Migration Strategies

  • Migration assessment
  • Data validation
  • Migration tools
  • Cutover planning
  • Optimization techniques

Module 22: Business Intelligence Integration

  • Looker overview
  • Dashboard integration
  • Data visualization
  • Reporting architecture
  • Analytics workflows

Module 23: Cost and Resource Management

  • Google Cloud pricing
  • Budget management
  • Resource optimization
  • Cost monitoring
  • Financial governance

Module 24: Advanced Data Engineering Best Practices

  • Solution architecture
  • Scalability planning
  • Security optimization
  • Operational excellence
  • Production-ready data solutions 
— 01.2 · Is it right for you?

Who it's for & what's included

Pick a delivery method to see exactly who it suits and everything you receive.

Who it's for

Classroom

Best for learners who want face-to-face tuition and to network with peers in person.

What's included

Everything you get

  • Live instructor on-site
  • Printed workbook & materials
  • Group exercises & case studies
Who it's for

Online Instructor-Led

Best for learners who want a live instructor and a fixed schedule, without the travel.

What's included

Everything you get

  • Live instructor via video call
  • Digital workbook & resources
  • Session recordings
Who it's for

Self-Paced

Best for self-motivated learners who need maximum flexibility around work and life.

What's included

Everything you get

  • On-demand video lessons
  • Interactive quizzes
  • 24/7 access on any device
— What you will master

Course Objectives

01

Design scalable data engineering solutions on Google Cloud

02

Build and manage batch and streaming data pipelines

03

Process large datasets using Google Cloud data services

04

Implement secure and governed data management practices

05

Optimize data processing performance and operational efficiency

06

Integrate analytics and machine learning into data workflows

07

Automate data engineering processes using cloud-native services

08

Design reliable, high-performance enterprise data architectures

— Questions answered

Frequently Asked Questions

Who should attend this course?
This course is designed for experienced data engineers, cloud engineers, analytics professionals, and IT specialists working with large-scale data solutions.
Do I need prior Google Cloud experience?
A working knowledge of cloud computing, SQL, and data processing concepts is recommended before enrolling.
Will this course include practical exercises?
Yes. The course combines theory with hands-on labs covering data ingestion, transformation, analytics, orchestration, and optimization using Google Cloud services.
Which Google Cloud services are covered?
The course covers BigQuery, Dataflow, Dataproc, Pub/Sub, Cloud Storage, Cloud Composer, Cloud SQL, Vertex AI, Looker, and other core data engineering services.
What skills will I gain from this course?
You will learn to build scalable data pipelines, process structured and unstructured data, optimize cloud data workloads, automate workflows, and design enterprise-grade data engineering solutions.
— Trusted by learners

What our delegates say

★★★★★

"The structure, the practice exams, the instructor — all top tier. Passed first try."

AS
Aarti SharmaSenior Project Manager · TCS
★★★★★

"Best training I have attended. The content is exactly what modern projects need."

JD
James DonovanProgramme Director · Capgemini
★★★★★

"24/7 support actually means 24/7 — got help on my mock exam at 2am. Worth every dollar."

MO
Maya OkaforPMO Lead · Standard Bank

★ 4.8 / 5 from 12,000+ verified learner reviews on Trustpilot & Google.

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