Cloud-Native Data Engineering on GCP Training Course

 Cloud-Native Data Engineering on Google Cloud Platform (GCP) Training Course 

Introduction 

Cloud-Native Data Engineering on Google Cloud Platform (GCP) Training Course is designed to equip participants with advanced skills in designing, developing, deploying, and managing scalable cloud-based data engineering solutions. The course explores modern cloud data architecture, distributed data processing, data pipeline automation, serverless computing, data integration, and real-time data streaming using Google Cloud technologies. Participants gain practical experience with BigQuery, Cloud Dataflow, Pub/Sub, Cloud Storage, Dataproc, Cloud Composer, and Dataform to build high-performance data platforms that support enterprise analytics, artificial intelligence, and machine learning applications. 

The training emphasizes cloud-native architecture, data lakehouse development, event-driven data processing, infrastructure automation, data governance, and cloud security best practices. Through practical exercises and international case studies, participants learn to implement reliable extract, transform, and load (ETL) and extract, load, and transform (ELT) workflows, optimize cloud data workloads, and manage enterprise-scale data environments. The course also addresses modern data engineering trends, including generative artificial intelligence integration, data observability, DevOps, continuous integration and continuous delivery (CI/CD), and cost-efficient cloud resource management. 

Course Objectives 

By the end of this course, participants will be able to: 

1. Design scalable cloud-native data engineering architectures using GCP.
2. Develop automated ETL and ELT data pipelines.
3. Implement real-time data streaming using Pub/Sub.
4. Manage enterprise datasets using BigQuery.
5. Build serverless data processing solutions using Dataflow.
6. Implement cloud data lake and lakehouse architectures.
7. Integrate artificial intelligence and machine learning workflows.
8. Apply data governance, security, and access management.
9. Optimize distributed data processing and query performance.
10. Automate infrastructure deployment using Infrastructure as Code.
11. Implement CI/CD pipelines for data engineering.
12. Monitor data pipelines using cloud observability tools.
13. Apply cloud cost optimization and operational best practices.

Organizational Benefits
 

1. Reduced infrastructure and data processing costs.
2. Improved enterprise data accessibility.
3. Faster business intelligence and analytics.
4. Enhanced real-time decision-making.
5. Improved data security and regulatory compliance.
6. Scalable enterprise data infrastructure.
7. Increased operational efficiency.
8. Automated data integration processes.
9. Better artificial intelligence readiness.
10. Improved organizational data governance.

Target Audiences
 

1. Data engineers and cloud data architects.
2. Database administrators and developers.
3. Business intelligence professionals.
4. Cloud solutions architects.
5. Data scientists and machine learning engineers.
6. IT infrastructure and DevOps professionals.
7. Enterprise analytics consultants.
8. Technology managers and digital transformation specialists.

Course Duration: 5 days

Course Modules

Module 1: Introduction to Cloud-Native Data Engineering
 

• Cloud-native architecture and distributed computing.
• GCP infrastructure and core services.
• Cloud Storage and data lake fundamentals.
• Serverless computing and microservices.
• Case study: Netflix-scale cloud data architecture principles.

Module 2: Data Ingestion and Integration
 

• Batch and real-time data ingestion.
• Cloud Pub/Sub messaging architecture.
• Data connectors and API integration.
• ETL and ELT pipeline development.
• Case study: Uber's real-time data ingestion approaches.

Module 3: BigQuery and Enterprise Data Warehousing
 

• BigQuery architecture and dataset management.
• SQL analytics and advanced querying.
• Partitioning and clustering optimization.
• Data warehouse performance management.
• Case study: Spotify-style large-scale analytics using cloud data warehouses.

Module 4: Apache Beam and Cloud Dataflow
 

• Distributed data processing fundamentals.
• Apache Beam pipeline development.
• Batch and streaming transformations.
• Dataflow autoscaling and optimization.
• Case study: Global financial transaction processing pipelines.

Module 5: Data Lakehouse and Distributed Processing
 

• Cloud data lake architecture.
• Dataproc and Apache Spark integration.
• Dataform and SQL-based transformations.
• Data cataloguing and metadata management.
• Case study: Retail enterprise lakehouse implementation.

Module 6: Workflow Orchestration and Automation
 

• Cloud Composer and Apache Airflow.
• Pipeline scheduling and dependencies.
• Workflow monitoring and error handling.
• Infrastructure as Code fundamentals.
• Case study: Automated global supply chain data workflows.

Module 7: Data Security, Governance and Observability
 

• Identity and Access Management (IAM).
• Encryption and sensitive data protection.
• Data quality and observability.
• Audit logging and regulatory compliance.
• Case study: Healthcare data governance using cloud security controls.

Module 8: Advanced Cloud-Native Data Engineering
 

• CI/CD and DataOps implementation.
• Machine learning pipeline integration.
• Generative artificial intelligence and BigQuery integration.
• Cloud performance and cost optimization.
• Case study: Google-scale cloud analytics and intelligent data processing.

Training Methodology
 

• Instructor-led presentations and technical demonstrations.
• Practical GCP laboratory exercises and guided projects.
• Real-world cloud data engineering simulations.
• International case studies and group discussions.
• Hands-on pipeline development and troubleshooting.
• Individual assessments and collaborative capstone projects.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.
 
b. Upon completion of training the participant will be issued with an Authorized Training Certificate
 
c. Course duration is flexible and the contents can be modified to fit any number of days.
 
d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.
 
e. One-year post-training support Consultation and Coaching provided after the course.
 
f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.