Data Infrastructure Automation Training Course

 Data Infrastructure Automation Training Course 

Introduction 

Data Infrastructure Automation Training Course is designed to equip professionals with the technical knowledge and practical skills required to automate, manage, scale, and optimize modern data infrastructure environments. As organizations increasingly adopt cloud computing, big data analytics, artificial intelligence, machine learning, and enterprise data platforms, infrastructure automation has become essential for improving operational efficiency, system reliability, scalability, and cost optimization. This course explores Infrastructure as Code (IaC), automated infrastructure provisioning, configuration management, cloud infrastructure orchestration, containerization, data pipeline automation, and DevOps practices. Participants will gain practical experience using industry-leading technologies such as Terraform, Ansible, Kubernetes, Docker, Git, Jenkins, and cloud-native automation services across Amazon Web Services, Microsoft Azure, and Google Cloud Platform. 

The training also examines advanced data infrastructure automation strategies, including automated deployment, infrastructure monitoring, security compliance, disaster recovery, resource orchestration, and continuous integration and continuous delivery (CI/CD). Through practical exercises and global industry case studies, participants will learn how to develop reusable automation frameworks, implement self-service infrastructure provisioning, manage hybrid and multi-cloud environments, and establish reliable data engineering operations. The course emphasizes modern DevOps automation, platform engineering, cloud infrastructure management, and operational excellence, enabling organizations to accelerate digital transformation, reduce manual workloads, strengthen infrastructure governance, and build resilient data ecosystems. 

Course Objectives 

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

1. Understand data infrastructure automation principles and architectures.
2. Implement Infrastructure as Code using Terraform and Ansible.
3. Automate cloud infrastructure provisioning and deployment.
4. Develop scalable infrastructure orchestration frameworks.
5. Apply DevOps and CI/CD automation practices.
6. Automate data platform configuration and management.
7. Implement Kubernetes and Docker infrastructure automation.
8. Strengthen infrastructure security and compliance automation.
9. Automate infrastructure monitoring and performance optimization.
10. Manage hybrid-cloud and multi-cloud infrastructure environments.
11. Implement automated backup and disaster recovery solutions.
12. Optimize infrastructure resource utilization and cloud expenditure.
13. Develop reusable automation workflows for enterprise data platforms.

Organizational Benefits
 

1. Reduced infrastructure deployment and operational costs.
2. Improved productivity through automated workflows.
3. Faster cloud infrastructure provisioning.
4. Enhanced data platform reliability and scalability.
5. Reduced human errors and configuration inconsistencies.
6. Improved infrastructure security and compliance.
7. Greater operational visibility and system monitoring.
8. Accelerated digital transformation initiatives.
9. Improved disaster recovery and business continuity.
10. Standardized infrastructure management practices.

Target Audiences
 

1. Data Engineers and Data Architects.
2. Cloud Infrastructure Engineers.
3. DevOps and Platform Engineers.
4. Systems Administrators and IT Managers.
5. Database Administrators.
6. Cloud Solutions Architects.
7. Site Reliability Engineers.
8. IT Infrastructure and Automation Specialists.

Course Duration: 5 days
 
Course Modules

Module 1: Foundations of Data Infrastructure Automation
 

• Principles and architecture of automated data infrastructure.
• Infrastructure as Code and configuration management fundamentals.
• Infrastructure lifecycle management and automation frameworks.
• Cloud-native infrastructure and enterprise data platforms.
• Automation governance, scalability, and operational efficiency.
• Case Study: Netflix's cloud infrastructure automation and scalable digital services.

Module 2: Infrastructure as Code and Provisioning
 

• Terraform configuration, providers, resources, and modules.
• Automated infrastructure provisioning and deployment.
• State management, version control, and reusable templates.
• Ansible playbooks and infrastructure configuration automation.
• Infrastructure testing, validation, and deployment workflows.
• Case Study: Spotify's infrastructure automation and cloud-native engineering practices.

Module 3: Cloud Infrastructure Automation
 

• Amazon Web Services infrastructure automation services.
• Microsoft Azure Resource Manager and Bicep templates.
• Google Cloud Deployment Manager alternatives and infrastructure automation.
• Automated networking, storage, and computing resource management.
• Hybrid-cloud and multi-cloud provisioning strategies.
• Case Study: Capital One's cloud transformation and automated infrastructure management.

Module 4: Containerization and Infrastructure Orchestration
 

• Docker containerization and automated deployment.
• Kubernetes cluster provisioning and configuration.
• Container orchestration and resource scheduling.
• Helm charts and automated application deployment.
• Infrastructure scaling and container security management.
• Case Study: Airbnb's use of containerization and scalable infrastructure technologies.

Module 5: DevOps and CI/CD Infrastructure Automation
 

• Continuous integration and continuous delivery pipelines.
• Jenkins, GitHub Actions, and GitLab CI/CD automation.
• Automated infrastructure testing and deployment.
• GitOps workflows and infrastructure version control.
• Release management and deployment optimization.
• Case Study: Google's DevOps practices and automated software delivery pipelines.

Module 6: Data Platform and Pipeline Automation
 

• Automated data infrastructure configuration and deployment.
• Data pipeline orchestration using Apache Airflow.
• Automated data warehouse and lakehouse provisioning.
• Integration of cloud storage and distributed computing platforms.
• Workflow scheduling, dependency management, and automation.
• Case Study: Uber's large-scale data infrastructure and automated data processing ecosystem.

Module 7: Infrastructure Monitoring, Security and Reliability
 

• Automated infrastructure monitoring and observability.
• Prometheus, Grafana, and centralized logging.
• Infrastructure security automation and access management.
• Automated compliance checks and vulnerability management.
• Incident response, backup automation, and disaster recovery.
• Case Study: Amazon's infrastructure reliability engineering and automated operational monitoring.

Module 8: Advanced Automation and Infrastructure Optimization
 

• Developing reusable enterprise automation frameworks.
• Automated resource scaling and performance optimization.
• Cloud cost management and resource allocation automation.
• Infrastructure policy enforcement and compliance governance.
• Future trends in AI-driven infrastructure automation and platform engineering.
• Case Study: Microsoft Azure's intelligent cloud operations and infrastructure optimization.

Training Methodology
 

• Expert-led presentations and interactive technical discussions.
• Practical Infrastructure as Code exercises and demonstrations.
• Hands-on Terraform, Ansible, Docker, and Kubernetes laboratories.
• Cloud infrastructure provisioning and automation simulations.
• International case studies and enterprise implementation scenarios.
• Group assignments, troubleshooting exercises, and knowledge assessments.
• Real-world infrastructure automation projects and demonstrations.

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.