Platform Engineering for Data Teams Training Course

 Platform Engineering for Data Teams Training Course 

Introduction 

Platform Engineering for Data Teams Training Course is designed to equip participants with the technical knowledge and practical skills required to build, manage, automate, and optimize modern data platforms. As organizations increasingly adopt cloud computing, artificial intelligence, big data analytics, and distributed data architectures, platform engineering has become essential for delivering scalable, secure, reliable, and self-service data infrastructure. This course explores modern data platform architecture, Infrastructure as Code (IaC), DataOps, DevOps, Kubernetes, container orchestration, cloud-native technologies, automated deployment, data pipeline management, and platform observability. Participants will learn how to develop reusable platform capabilities that simplify data engineering workflows, improve developer experience, and accelerate enterprise data delivery. 

The course also examines how leading organizations implement internal developer platforms, data infrastructure automation, platform-as-a-product strategies, and self-service data environments to improve operational efficiency and business agility. Through practical exercises and international case studies, participants will explore cloud platforms, CI/CD automation, infrastructure provisioning, data governance, security engineering, and reliability management. Particular emphasis is placed on building scalable data ecosystems using modern engineering practices, reducing infrastructure complexity, improving collaboration between data engineers and platform teams, and enabling AI-ready data infrastructure. By completing this training, participants will be better positioned to design and operate enterprise-grade data platforms that support digital transformation, real-time analytics, machine learning, and sustainable data operations. 

Course Objectives 

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

1. Understand platform engineering principles and modern data platform architecture.
2. Design scalable internal developer platforms for data engineering teams.
3. Implement Infrastructure as Code and automated infrastructure provisioning.
4. Apply DevOps and DataOps practices to data platform operations.
5. Deploy containerized data workloads using Docker and Kubernetes.
6. Develop automated CI/CD pipelines for data infrastructure.
7. Implement cloud-native data engineering environments.
8. Improve developer experience through self-service platform capabilities.
9. Apply data security, governance, and access management frameworks.
10. Implement platform observability, monitoring, and performance optimization.
11. Automate data pipeline deployment and orchestration.
12. Strengthen platform reliability through Site Reliability Engineering practices.
13. Optimize platform scalability, cost efficiency, and operational resilience.

Organizational Benefits
 

1. Reduced infrastructure provisioning costs through automation.
2. Faster data engineering development and deployment cycles.
3. Improved platform reliability and operational performance.
4. Standardized enterprise data engineering environments.
5. Enhanced data security and governance compliance.
6. Increased productivity through self-service infrastructure.
7. Reduced operational complexity and technical debt.
8. Improved collaboration between development and operations teams.
9. Scalable infrastructure supporting AI and advanced analytics.
10. Better resource utilization and cloud cost management.

Target Audiences
 

1. Data engineers and senior data engineers.
2. Platform engineers and infrastructure engineers.
3. DevOps and DataOps professionals.
4. Cloud architects and solutions architects.
5. Data architects and enterprise architects.
6. Site Reliability Engineers (SREs).
7. IT infrastructure and operations managers.
8. Technical leads and data engineering managers.

Course Duration: 10 days

Course Modules

Module 1: Introduction to Platform Engineering for Data Teams
 

• Platform engineering principles and operating models.
• Evolution of modern data infrastructure.
• Internal Developer Platforms (IDPs).
• Platform-as-a-product strategies.
• Self-service data engineering environments.
• Case study: Spotify's Backstage developer platform.

Module 2: Modern Data Platform Architecture
 

• Enterprise data platform architecture.
• Data lakes, warehouses, and lakehouse ecosystems.
• Distributed computing and microservices.
• Data mesh and domain-oriented architecture.
• Scalable data infrastructure design.
• Case study: Netflix's cloud-based data architecture.

Module 3: Infrastructure as Code for Data Platforms
 

• Infrastructure automation fundamentals.
• Terraform and configuration management.
• Automated cloud resource provisioning.
• Reusable infrastructure templates.
• Infrastructure version control and testing.
• Case study: Airbnb's infrastructure automation practices.

Module 4: Cloud-Native Data Platform Engineering
 

• AWS, Microsoft Azure, and Google Cloud.
• Cloud-native infrastructure deployment.
• Managed data engineering services.
• Hybrid and multi-cloud architectures.
• Cloud resource optimization.
• Case study: Capital One's cloud transformation.

Module 5: Containerization and Kubernetes
 

• Docker containers for data workloads.
• Kubernetes architecture and orchestration.
• Container deployment and management.
• Helm charts and workload configuration.
• Kubernetes scalability and resource management.
• Case study: Spotify's containerized infrastructure.

Module 6: CI/CD Automation for Data Platforms
 

• Continuous Integration and Continuous Delivery.
• Automated infrastructure deployment pipelines.
• GitOps workflows and version control.
• Pipeline testing and release management.
• Deployment governance and rollback strategies.
• Case study: Google's automated software delivery practices.

Module 7: DataOps and Platform Automation
 

• DataOps principles and implementation.
• Automated data pipeline deployment.
• Workflow orchestration using Apache Airflow.
• Data quality automation and validation.
• Collaboration across data engineering teams.
• Case study: Uber's large-scale data processing ecosystem.

Module 8: Self-Service Data Infrastructure
 

• Self-service platform architecture.
• Data environment provisioning.
• Developer portals and service catalogs.
• Reusable platform templates and APIs.
• Automated access and resource management.
• Case study: Spotify's Backstage service catalog.

Module 9: Data Security and Platform Governance
 

• Identity and Access Management (IAM).
• Role-based access control and authentication.
• Data encryption and secrets management.
• Infrastructure security and compliance.
• Governance policies and audit automation.
• Case study: Microsoft's enterprise cloud security practices.

Module 10: Platform Observability and Monitoring
 

• Infrastructure monitoring and telemetry.
• Logs, metrics, and distributed tracing.
• Prometheus and Grafana implementation.
• Alert management and incident detection.
• Platform performance analytics.
• Case study: Google SRE monitoring practices.

Module 11: Site Reliability Engineering for Data Platforms
 

• Service Level Objectives and Indicators.
• Reliability engineering principles.
• Incident response and root-cause analysis.
• Capacity planning and resilience engineering.
• Disaster recovery and business continuity.
• Case study: Amazon's large-scale reliability engineering.

Module 12: Data Platform Integration and Interoperability
 

• Data platform integration strategies.
• API management and service communication.
• Event-driven architecture and messaging.
• Apache Kafka and streaming integration.
• Cross-platform data interoperability.
• Case study: LinkedIn's distributed data infrastructure.

Module 13: AI-Ready Data Platform Engineering
 

• Infrastructure for machine learning workloads.
• MLOps and platform integration.
• GPU infrastructure and resource scheduling.
• Feature stores and model deployment pipelines.
• Scalable infrastructure for generative AI.
• Case study: Google's Vertex AI ecosystem.

Module 14: Platform FinOps and Cost Optimization
 

• Cloud financial management principles.
• Infrastructure cost monitoring.
• Resource allocation and optimization.
• Automated scaling and capacity management.
• Platform usage metrics and cost accountability.
• Case study: Netflix's cloud resource optimization practices.

Module 15: Platform Engineering Strategy and Implementation
 

• Platform maturity assessment and roadmaps.
• Enterprise platform operating models.
• Platform adoption and change management.
• Performance measurement and continuous improvement.
• Future trends in data platform engineering.
• Case study: Spotify's platform engineering transformation.

Training Methodology
 

• Instructor-led presentations and interactive technical discussions.
• Practical laboratory sessions involving platform engineering tools.
• Hands-on Infrastructure as Code and Kubernetes exercises.
• Group projects involving internal developer platform development.
• International case studies and enterprise implementation scenarios.
• Practical assessments, demonstrations, and knowledge-sharing sessions.

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.