Course Introduction
Data centres are critical foundations of modern digital services, supporting applications, databases, cloud platforms, artificial intelligence, enterprise systems, telecommunications, and increasingly data-intensive workloads. As infrastructure environments become more complex, organizations need advanced analytical capabilities to understand resource utilization, identify capacity constraints, predict future demand, optimize operational efficiency, and maintain reliable service delivery.
This training course provides a comprehensive framework for applying data analytics, monitoring, forecasting, and infrastructure intelligence to data centre environments. Participants will learn how to collect, integrate, analyze, and visualize infrastructure telemetry from servers, storage systems, networks, virtualization platforms, cooling systems, power infrastructure, and other operational technologies to support evidence-based capacity and performance decisions.
The course explores the relationship between infrastructure performance, workload behavior, resource consumption, availability, and capacity planning. Participants will learn how to analyze CPU, memory, storage, network throughput, input/output activity, virtualization utilization, power consumption, temperature, cooling requirements, and other operational indicators to identify trends, anomalies, inefficiencies, bottlenecks, and emerging infrastructure risks.
Particular emphasis is placed on capacity intelligence and predictive planning. Rather than relying only on historical utilization reports, participants will explore forecasting techniques, trend analysis, scenario modelling, workload profiling, threshold analysis, and predictive analytics for anticipating infrastructure requirements. These techniques can support decisions about procurement, scaling, workload placement, consolidation, cloud migration, infrastructure refresh, and data centre expansion.
The course also addresses the financial, operational, environmental, security, and governance dimensions of infrastructure analytics. Participants will examine how capacity decisions affect capital expenditure, operating costs, energy consumption, resilience, sustainability, service-level objectives, and technology lifecycle management. They will also explore how dashboards, automated alerts, analytics pipelines, and intelligent recommendations can improve infrastructure visibility and operational decision-making.
By the end of the course, participants will be able to design and apply practical data centre analytics and capacity intelligence approaches for modern infrastructure environments. The course also examines emerging developments such as AI-driven infrastructure operations, AIOps, digital twins, intelligent workload orchestration, GPU capacity planning, edge computing, liquid cooling, energy-aware scheduling, and predictive infrastructure management.
Duration
10 days
Who Should Attend
-
Data centre managers responsible for infrastructure operations, performance, availability, and capacity planning.
-
IT infrastructure managers overseeing servers, storage, networks, virtualization, and enterprise computing environments.
-
Systems administrators responsible for monitoring infrastructure utilization and operational performance.
-
Network engineers analyzing network capacity, traffic patterns, congestion, and infrastructure requirements.
-
Data engineers supporting infrastructure telemetry collection, processing, and analytical pipelines.
-
Cloud engineers responsible for capacity management across public, private, and hybrid cloud environments.
-
Enterprise architects involved in infrastructure planning, modernization, scalability, and technology lifecycle decisions.
-
Infrastructure capacity planners responsible for forecasting demand and resource requirements.
-
IT operations professionals seeking to strengthen analytical approaches to infrastructure management.
-
Site reliability engineers responsible for availability, performance, resilience, and service capacity.
-
DevOps and platform engineering professionals managing scalable computing environments.
-
FinOps professionals analyzing infrastructure consumption, cloud expenditure, and resource optimization.
-
Data analysts and business intelligence professionals developing infrastructure performance dashboards.
-
IT service management professionals responsible for service performance, capacity, and operational reporting.
-
Facilities and data centre engineering professionals involved in power, cooling, environmental monitoring, and physical capacity.
-
Sustainability professionals assessing energy efficiency and environmental performance in computing facilities.
-
Cybersecurity professionals interested in infrastructure telemetry, anomaly detection, and operational intelligence.
-
Technology leaders planning data centre modernization, expansion, consolidation, or hybrid infrastructure strategies.
Course Objectives
-
Develop a comprehensive understanding of data centre analytics, infrastructure intelligence, capacity management, and operational performance measurement.
-
Identify and analyze infrastructure telemetry from computing, storage, networking, virtualization, facilities, power, cooling, and environmental systems.
-
Develop meaningful infrastructure performance indicators for utilization, availability, capacity, efficiency, reliability, and service-level monitoring.
-
Apply statistical and analytical techniques to identify infrastructure trends, bottlenecks, anomalies, inefficiencies, and emerging capacity constraints.
-
Build infrastructure capacity forecasts using historical utilization, workload growth, business demand, seasonality, and scenario-based planning.
-
Evaluate CPU, memory, storage, network, virtualization, GPU, power, cooling, and facility capacity requirements across different workloads.
-
Design dashboards and analytical reports that provide operational teams and executives with actionable infrastructure intelligence.
-
Apply predictive analytics and machine learning techniques to support proactive capacity planning, anomaly detection, and infrastructure optimization.
-
Analyze infrastructure costs and resource consumption to support procurement, consolidation, cloud optimization, and technology investment decisions.
-
Integrate capacity intelligence with availability, resilience, disaster recovery, sustainability, cybersecurity, and service management requirements.
-
Develop governance approaches for infrastructure data quality, monitoring standards, access controls, analytical models, and capacity planning processes.
-
Evaluate emerging technologies including AIOps, digital twins, intelligent workload orchestration, GPU infrastructure, edge computing, and energy-aware data centres.
Comprehensive Course Outline
Module 1: Foundations Of Data Centre Analytics And Capacity Intelligence
-
Principles, objectives, and organizational value of data centre analytics and infrastructure capacity intelligence.
-
Relationship between infrastructure monitoring, operational analytics, capacity management, service delivery, and business continuity.
-
Key infrastructure domains including compute, storage, networking, virtualization, facilities, power, cooling, and environmental systems.
-
Roles of infrastructure, operations, data, finance, security, facilities, and business teams in capacity intelligence.
Module 2: Data Centre Infrastructure Architecture And Workload Fundamentals
-
Server, storage, network, virtualization, container, cloud, and physical infrastructure architectures in modern data centres.
-
Understanding workload characteristics, resource dependencies, utilization patterns, and infrastructure consumption behavior.
-
Mapping applications and business services to underlying infrastructure resources and capacity requirements.
-
Identifying infrastructure dependencies, shared resources, single points of failure, and critical workload relationships.
Module 3: Infrastructure Telemetry And Data Collection
-
Sources of infrastructure telemetry including servers, hypervisors, storage systems, switches, routers, sensors, and management platforms.
-
Collecting CPU, memory, storage, network, I/O, power, temperature, cooling, and environmental performance data.
-
Monitoring protocols, APIs, agents, logs, metrics, events, traces, and time-series data for infrastructure analytics.
-
Designing reliable telemetry collection processes that preserve data quality, frequency, context, and historical information.
Module 4: Infrastructure Data Engineering And Data Quality
-
Designing pipelines for collecting, integrating, transforming, storing, and processing infrastructure monitoring data.
-
Managing time-series infrastructure data, event streams, operational logs, metrics, metadata, and configuration information.
-
Identifying missing values, inconsistent measurements, duplicate events, incorrect timestamps, and unreliable telemetry.
-
Establishing infrastructure data quality rules, metadata standards, retention policies, and validation processes.
Module 5: Infrastructure Performance Analytics
-
Analyzing CPU utilization, memory consumption, storage performance, network throughput, latency, and I/O behavior.
-
Developing baseline performance profiles for infrastructure components, workloads, applications, and services.
-
Identifying performance bottlenecks, resource contention, saturation points, and abnormal utilization patterns.
-
Applying statistical analysis and comparative techniques to understand infrastructure performance across environments.
Module 6: Capacity Measurement And Resource Utilization
-
Principles of capacity measurement, utilization thresholds, headroom analysis, and available resource calculation.
-
Assessing compute, memory, storage, network, virtualization, GPU, and facility capacity against operational requirements.
-
Identifying underutilized, overutilized, stranded, and inefficiently allocated infrastructure resources.
-
Developing capacity indicators that connect infrastructure resources with workloads, business services, and operational demand.
Module 7: Capacity Forecasting And Demand Planning
-
Developing infrastructure demand forecasts using historical utilization, growth rates, workload trends, and business requirements.
-
Applying time-series analysis, trend modelling, seasonality analysis, and scenario-based capacity forecasting.
-
Estimating future requirements for computing, storage, network, GPU, power, cooling, and physical data centre resources.
-
Developing capacity plans that account for uncertainty, growth variations, technology changes, and business expansion.
Module 8: Infrastructure Anomaly Detection And Predictive Analytics
-
Identifying abnormal infrastructure behavior using statistical thresholds, baselines, rules, and machine learning techniques.
-
Detecting unusual resource consumption, performance degradation, hardware behavior, network anomalies, and capacity risks.
-
Applying predictive analytics to identify potential infrastructure failures, saturation events, and service-impacting conditions.
-
Developing alert prioritization and predictive notification approaches that reduce operational noise and support proactive intervention.
Module 9: Virtualization, Cloud And Hybrid Capacity Intelligence
-
Analyzing virtual machine, container, hypervisor, cluster, and shared-resource utilization patterns.
-
Understanding capacity planning considerations across public cloud, private cloud, hybrid environments, and multi-cloud architectures.
-
Evaluating cloud resource consumption, elasticity, autoscaling behavior, reservations, quotas, and workload placement.
-
Integrating on-premises and cloud telemetry to develop unified capacity and infrastructure intelligence.
Module 10: Storage, Network And GPU Capacity Analytics
-
Analyzing storage capacity, IOPS, throughput, latency, utilization, replication, backup, and data growth patterns.
-
Evaluating network bandwidth, traffic flows, latency, packet behavior, congestion, and network infrastructure utilization.
-
Assessing GPU utilization, accelerator workloads, memory requirements, scheduling, and capacity constraints for AI applications.
-
Developing resource forecasts for increasingly data-intensive analytics, machine learning, generative AI, and high-performance computing workloads.
Module 11: Data Centre Power, Cooling And Sustainability Analytics
-
Monitoring power consumption, power utilization effectiveness, temperature, humidity, cooling demand, and environmental conditions.
-
Analyzing energy efficiency and identifying opportunities for reducing unnecessary infrastructure and facility consumption.
-
Connecting workload behavior and infrastructure utilization with power, cooling, carbon, and sustainability indicators.
-
Developing analytics approaches for energy-aware capacity planning, environmental optimization, and sustainable data centre operations.
Module 12: Dashboards, Visualization And Operational Intelligence
-
Designing infrastructure dashboards for real-time monitoring, historical analysis, capacity planning, and executive reporting.
-
Developing key performance indicators for utilization, availability, performance, capacity headroom, efficiency, and operational risk.
-
Applying visualization techniques to reveal trends, bottlenecks, anomalies, dependencies, and infrastructure growth patterns.
-
Creating role-specific intelligence views for data centre operators, engineers, managers, executives, finance teams, and sustainability stakeholders.
Module 13: Capacity Optimization And Infrastructure Efficiency
-
Identifying opportunities for server consolidation, workload redistribution, resource right-sizing, and infrastructure optimization.
-
Evaluating infrastructure utilization against procurement, licensing, maintenance, energy, and operational costs.
-
Applying capacity optimization techniques to improve resource efficiency while maintaining performance and resilience requirements.
-
Connecting infrastructure optimization with cloud cost management, FinOps, lifecycle planning, and technology investment decisions.
Module 14: Capacity Governance, Resilience And Risk Management
-
Establishing capacity management policies, ownership structures, thresholds, review cycles, and escalation mechanisms.
-
Integrating capacity intelligence with availability management, disaster recovery, business continuity, and service-level objectives.
-
Identifying risks from resource saturation, infrastructure failure, insufficient headroom, dependency concentration, and demand uncertainty.
-
Developing governance dashboards, capacity reviews, risk registers, and decision processes for infrastructure planning.
Module 15: Automated Operations, AIOps And Intelligent Infrastructure
-
Applying AIOps techniques to correlate infrastructure metrics, logs, events, dependencies, and operational incidents.
-
Developing automated anomaly detection, predictive alerting, root-cause analysis, and infrastructure remediation workflows.
-
Using machine learning to identify workload patterns, predict resource requirements, and optimize infrastructure allocation.
-
Integrating infrastructure analytics with DevOps, platform engineering, observability, automation, and intelligent operations.
Module 16: Emerging Trends And Future Of Data Centre Capacity Intelligence
-
Exploring AI-driven data centre operations, autonomous infrastructure management, intelligent workload orchestration, and predictive capacity optimization.
-
Evaluating digital twins, simulation models, and advanced scenario environments for data centre planning and operational decision-making.
-
Assessing emerging requirements for GPU clusters, AI factories, edge computing, high-density computing, liquid cooling, and advanced power systems.
-
Preparing for future challenges involving energy constraints, sustainability, infrastructure complexity, cybersecurity, technology evolution, and rapidly increasing computational demand.
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.
Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment:
Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
