Site Reliability Engineering for Data Platforms Training Course

 Site Reliability Engineering for Data Platforms Training Course 

Introduction 

Site Reliability Engineering for Data Platforms Training Course is designed to equip data engineers, cloud professionals, DevOps practitioners, and IT infrastructure specialists with the technical expertise required to build, operate, monitor, and maintain highly reliable data platforms. The course explores Site Reliability Engineering (SRE) principles, data platform observability, service level objectives, service level indicators, service level agreements, incident management, automated infrastructure management, and continuous improvement. Participants will develop practical capabilities in ensuring data availability, scalability, fault tolerance, performance optimization, and operational resilience across modern cloud-native data environments. 

The training emphasizes modern data engineering technologies, distributed systems reliability, cloud infrastructure automation, data pipeline monitoring, disaster recovery, and proactive incident prevention. Through practical exercises and global industry case studies, participants will examine how leading organizations manage large-scale data infrastructure, minimize downtime, improve data quality, and maintain business continuity. The course also explores artificial intelligence-driven operations, predictive monitoring, Kubernetes reliability, infrastructure as code, and automated recovery strategies to support dependable enterprise data ecosystems. 

Course Objectives 

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

1. Apply Site Reliability Engineering principles to modern data platforms.
2. Design highly available and scalable data infrastructure.
3. Implement service level indicators, objectives, and agreements.
4. Improve data pipeline reliability and operational performance.
5. Apply advanced observability and monitoring frameworks.
6. Automate incident detection, response, and resolution.
7. Implement fault tolerance and disaster recovery strategies.
8. Optimize distributed data processing environments.
9. Apply DevOps and DataOps automation practices.
10. Manage Kubernetes-based data platform reliability.
11. Implement infrastructure as code and configuration management.
12. Use artificial intelligence for predictive operations and anomaly detection.
13. Establish continuous reliability improvement and operational governance.

Organizational Benefits
 

1. Reduced data platform downtime and operational disruptions.
2. Improved enterprise data availability and accessibility.
3. Lower infrastructure maintenance and incident management costs.
4. Enhanced data pipeline performance and reliability.
5. Improved business continuity and disaster preparedness.
6. Stronger cloud infrastructure scalability and resilience.
7. Better compliance, security, and operational governance.
8. Increased engineering productivity through automation.
9. Improved customer experience through dependable data services.
10. Stronger organizational capacity for digital transformation.

Target Audiences
 

1. Site Reliability Engineers and DevOps Engineers.
2. Data Engineers and Data Platform Architects.
3. Cloud Infrastructure and Systems Administrators.
4. DataOps and Platform Engineering Professionals.
5. Database Administrators and Data Architects.
6. IT Operations and Infrastructure Managers.
7. Software Engineers managing distributed systems.
8. Technology Consultants and Enterprise IT Managers.

Course Duration: 5 days

Course Modules

Module 1: Foundations of Site Reliability Engineering
 

• SRE principles, evolution, and application in enterprise data platforms.
• Reliability engineering, scalability, availability, and resilience fundamentals.
• Error budgets, toil reduction, and operational excellence.
• SRE integration with DevOps, DataOps, and platform engineering.
• Reliability culture, governance, and continuous improvement.
• Case study: Google's implementation of SRE principles across large-scale distributed infrastructure.

Module 2: Data Platform Architecture and Reliability
 

• Designing resilient cloud-native data platform architectures.
• Distributed systems, microservices, and data processing reliability.
• High availability, redundancy, and fault-tolerant infrastructure.
• Scalability strategies for enterprise data warehouses and lakes.
• Managing dependencies and preventing single points of failure.
• Case study: Netflix's distributed cloud architecture and resilience engineering.

Module 3: Service Level Management and Reliability Metrics
 

• Defining service level indicators, objectives, and agreements.
• Establishing data availability, freshness, latency, and accuracy metrics.
• Error budgets and reliability-based decision-making.
• Performance benchmarking and capacity planning.
• Reliability reporting and stakeholder communication.
• Case study: Google's SRE service level objectives and error budget practices.

Module 4: Data Observability and Performance Monitoring
 

• Implementing end-to-end data platform observability.
• Monitoring infrastructure, pipelines, databases, and workloads.
• Log management, distributed tracing, and metrics collection.
• Anomaly detection and artificial intelligence-driven monitoring.
• Tools including Prometheus, Grafana, and OpenTelemetry.
• Case study: Uber's large-scale data observability and monitoring practices.

Module 5: Incident Management and Automated Recovery
 

• Incident identification, classification, and prioritization.
• Root cause analysis and blameless post-incident reviews.
• Automated alerting, escalation, and incident response.
• Disaster recovery, backup management, and business continuity.
• Developing runbooks and automated remediation workflows.
• Case study: Amazon's operational incident management and recovery practices.

Module 6: Infrastructure Automation and Platform Operations
 

• Infrastructure as code using Terraform and Ansible.
• Kubernetes orchestration and container reliability management.
• Configuration management and automated deployment.
• Continuous integration and continuous delivery for data platforms.
• Capacity management, resource optimization, and auto-scaling.
• Case study: Spotify's platform engineering and infrastructure automation practices.

Module 7: Data Pipeline Reliability and Security
 

• Building fault-tolerant batch and real-time data pipelines.
• Managing data quality, integrity, and pipeline dependencies.
• Securing data infrastructure and managing access controls.
• Implementing encryption, secrets management, and compliance controls.
• Pipeline testing, recovery, and automated validation.
• Case study: LinkedIn's distributed data processing and pipeline reliability practices.

Module 8: Advanced SRE Strategies and Continuous Improvement
 

• Predictive analytics and artificial intelligence for IT operations.
• Reliability testing, chaos engineering, and resilience validation.
• Cost optimization and cloud resource efficiency.
• SRE maturity assessments and operational performance reviews.
• Developing enterprise reliability roadmaps and improvement strategies.
• Case study: Netflix's Chaos Engineering approach to improving system resilience.

Training Methodology
 

• Instructor-led presentations and interactive technical discussions.
• Practical exercises involving data platform reliability scenarios.
• Hands-on demonstrations using industry-standard SRE tools.
• Group assignments involving incident response and root cause analysis.
• International case studies and enterprise problem-solving exercises.
• Simulation-based learning covering monitoring, recovery, and resilience testing.

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.