Course Introduction
The Data Platform Capacity Planning And Performance Management Training Course provides a comprehensive understanding of how organizations can plan, measure, optimize, and continuously manage the capacity and performance of modern data platforms. The course examines the relationship between data volumes, workloads, compute resources, storage, memory, networking, concurrency, throughput, latency, and application requirements. Participants will learn how to establish capacity baselines and performance targets that support reliable and scalable data services.
The course explores capacity planning across databases, data warehouses, data lakes, lakehouses, distributed processing platforms, streaming systems, and cloud-native data architectures. Participants will learn how to assess current resource utilization, analyze historical growth patterns, forecast future demand, identify capacity constraints, and determine when infrastructure should be expanded, reduced, or reconfigured. Particular attention is given to workload characteristics, seasonal demand, business growth, data ingestion rates, query volumes, processing windows, and changing application requirements.
Performance management is examined as an ongoing discipline involving measurement, monitoring, diagnosis, optimization, and continuous improvement. Participants will learn how to define service-level objectives and performance indicators for data platforms, including latency, throughput, concurrency, resource utilization, query response time, job completion time, ingestion lag, and availability. The training introduces practical approaches for identifying performance bottlenecks across compute, storage, memory, networking, databases, queries, pipelines, and distributed processing components.
The course also addresses performance testing, workload modeling, benchmarking, capacity forecasting, and what-if analysis. Participants will learn how to simulate expected workloads, evaluate scaling requirements, identify saturation points, and assess the impact of architectural or configuration changes. Techniques for baseline creation, load testing, stress testing, capacity modeling, trend analysis, and performance benchmarking will help participants make evidence-based infrastructure decisions rather than relying solely on reactive troubleshooting.
Effective capacity management requires integration with cost, governance, reliability, and operational planning. The course therefore examines resource rightsizing, autoscaling, workload scheduling, storage optimization, partitioning, caching, query tuning, resource isolation, and cloud consumption management. Participants will also explore how capacity decisions affect cloud expenditure, resilience, disaster recovery, data quality, security, and service availability, enabling organizations to balance performance requirements with financial and operational constraints.
The course concludes with advanced approaches for highly scalable and intelligent data platforms, including predictive capacity planning, automated scaling, real-time performance management, distributed architectures, AI and machine learning workloads, multi-cloud environments, and autonomous optimization. Participants will develop practical frameworks for establishing capacity plans, monitoring performance, anticipating future requirements, and maintaining data platforms that remain responsive, reliable, cost-efficient, and scalable as workloads and business needs evolve.
Duration
10 days
Who Should Attend
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Data platform engineers responsible for capacity and performance management.
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Data architects designing scalable and high-performance data infrastructures.
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Database administrators managing database capacity, workloads, and performance.
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Cloud engineers responsible for scalable cloud data environments.
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Data engineers managing processing pipelines and platform resources.
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Data warehouse administrators monitoring analytical workload performance.
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Data lake and lakehouse engineers managing large-scale data platforms.
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Systems and infrastructure engineers responsible for resource planning and utilization.
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DevOps and platform engineering professionals managing data infrastructure.
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Performance engineers conducting workload analysis and optimization.
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IT managers responsible for infrastructure capacity, availability, and performance.
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Cloud FinOps professionals evaluating resource utilization and infrastructure efficiency.
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Business intelligence and analytics professionals working with high-volume workloads.
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Machine learning engineers managing compute-intensive data and model workloads.
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Enterprise architects planning future data platform infrastructure requirements.
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Technology leaders responsible for scalability, service reliability, and data platform investment.
Course Objectives
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Understand the principles, processes, and methodologies used for capacity planning across modern data platforms.
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Assess compute, storage, memory, networking, database, and processing resources against current and future workloads.
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Establish meaningful performance baselines, service-level objectives, service-level indicators, and operational targets.
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Analyze workload patterns, utilization trends, concurrency, throughput, latency, and resource saturation.
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Develop capacity forecasts using historical data, growth assumptions, business projections, and workload models.
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Identify performance bottlenecks across databases, queries, storage systems, pipelines, networks, and distributed platforms.
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Apply workload testing, benchmarking, load testing, stress testing, and what-if analysis to validate capacity requirements.
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Optimize data-platform resources through right-sizing, autoscaling, workload scheduling, caching, partitioning, and query tuning.
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Establish monitoring and alerting frameworks that provide early warning of performance degradation and capacity constraints.
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Integrate capacity planning with cloud cost management, resilience, availability, disaster recovery, and infrastructure governance.
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Plan scalable architectures capable of supporting increasing data volumes, concurrent users, analytical workloads, and AI applications.
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Develop continuous capacity and performance management frameworks that adapt to changing technical and business requirements.
Comprehensive Course Outline
Module 1: Foundations Of Data Platform Capacity Planning
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Principles of capacity planning and its role in maintaining reliable, scalable, and efficient data platforms.
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Understanding capacity dimensions including compute, memory, storage, networking, throughput, concurrency, and processing time.
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Relationships between workload demand, resource supply, platform architecture, performance, and service availability.
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Capacity management lifecycle covering assessment, forecasting, planning, implementation, monitoring, and continuous improvement.
Module 2: Data Platform Workload Analysis And Profiling
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Identifying workload types across transactional databases, analytical platforms, batch processing, streaming, and machine learning.
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Analyzing workload volume, frequency, concurrency, data size, processing complexity, and resource requirements.
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Building workload profiles that capture peak, average, seasonal, scheduled, and unpredictable demand patterns.
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Establishing workload classifications to support capacity planning, prioritization, resource allocation, and performance management.
Module 3: Capacity Baselines, Metrics And Performance Objectives
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Developing capacity baselines for compute, storage, memory, network utilization, throughput, and platform activity.
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Defining service-level indicators and objectives for latency, availability, throughput, processing time, and workload completion.
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Selecting performance metrics that accurately reflect platform health, user experience, and business requirements.
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Establishing thresholds, targets, alert levels, and measurement frameworks for proactive capacity management.
Module 4: Compute And Resource Capacity Planning
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Planning CPU, memory, virtual machines, containers, clusters, serverless resources, and specialized compute capacity.
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Evaluating resource utilization, saturation, idle capacity, overprovisioning, and underprovisioning.
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Right-sizing resources based on workload characteristics, performance requirements, growth expectations, and availability objectives.
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Planning compute capacity for batch, interactive, streaming, data engineering, and machine learning workloads.
Module 5: Storage Capacity Planning And Data Growth Management
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Forecasting storage requirements across databases, object storage, data lakes, warehouses, backups, and archives.
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Analyzing data growth rates, retention requirements, replication, compression, partitioning, and storage consumption patterns.
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Planning storage capacity for structured, semi-structured, unstructured, analytical, and high-volume datasets.
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Managing storage expansion, tiering, lifecycle policies, archival, and capacity thresholds.
Module 6: Network And Data Transfer Capacity Planning
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Understanding network bandwidth, latency, throughput, connection capacity, and data-transfer requirements for data platforms.
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Planning network capacity for ingestion, replication, distributed processing, cloud services, and analytical workloads.
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Identifying network bottlenecks caused by excessive data movement, congestion, architecture, or inefficient workload placement.
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Evaluating regional, hybrid, multi-cloud, and distributed network requirements for scalable data platforms.
Module 7: Database And Query Performance Management
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Monitoring database performance through query latency, execution time, concurrency, transactions, locks, and resource utilization.
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Identifying inefficient queries, indexing problems, contention, excessive scans, poor joins, and inefficient data access patterns.
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Applying indexing, partitioning, caching, query optimization, clustering, and physical design improvements.
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Establishing database performance baselines and proactive optimization processes for changing workloads.
Module 8: Data Pipeline And Processing Performance
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Measuring ingestion rates, transformation throughput, processing latency, job duration, queue time, and pipeline reliability.
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Identifying bottlenecks across extraction, transformation, loading, orchestration, storage, and processing stages.
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Optimizing parallelism, scheduling, resource allocation, partitioning, batching, and pipeline architecture.
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Planning processing capacity for batch, near-real-time, event-driven, and continuous data workloads.
Module 9: Performance Testing, Benchmarking And Workload Modeling
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Principles of load testing, stress testing, endurance testing, scalability testing, and performance benchmarking.
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Developing representative workload models for evaluating platform behavior under normal and peak conditions.
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Identifying saturation points, performance thresholds, bottlenecks, and scaling requirements through controlled testing.
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Comparing architectural configurations and optimization strategies using measurable performance benchmarks.
Module 10: Capacity Forecasting And Predictive Planning
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Using historical utilization, data growth, workload trends, business projections, and seasonal patterns for capacity forecasting.
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Developing short-term, medium-term, and long-term capacity plans for evolving data environments.
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Applying statistical forecasting, trend analysis, scenario modeling, and what-if analysis to anticipate future requirements.
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Managing uncertainty, unexpected growth, changing workloads, and emerging data-intensive applications.
Module 11: Cloud Scaling And Elastic Capacity Management
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Understanding vertical scaling, horizontal scaling, autoscaling, elastic resources, and cloud-native capacity models.
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Designing scaling policies based on utilization, workload queues, latency, throughput, and demand patterns.
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Evaluating managed services, serverless platforms, elastic warehouses, and distributed cloud architectures.
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Balancing scalability, performance, availability, operational complexity, and cloud infrastructure costs.
Module 12: Monitoring, Observability And Performance Alerting
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Designing monitoring frameworks for infrastructure, databases, pipelines, storage, networks, queries, and applications.
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Using metrics, logs, traces, events, dashboards, and alerts to establish comprehensive data-platform observability.
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Detecting performance degradation, capacity exhaustion, abnormal resource behavior, and emerging bottlenecks.
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Developing proactive alerting, escalation, incident response, and continuous performance monitoring processes.
Module 13: Capacity Optimization, Rightsizing And Resource Efficiency
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Identifying overprovisioned, underutilized, idle, and inefficiently allocated data-platform resources.
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Applying rightsizing, workload scheduling, resource pooling, caching, partitioning, compression, and storage optimization.
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Balancing resource utilization against performance targets, resilience requirements, and future growth.
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Integrating capacity optimization with cloud cost management and FinOps practices.
Module 14: High Availability, Resilience And Disaster Recovery Capacity
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Planning capacity requirements for redundancy, replication, failover, backup, recovery, and high-availability architectures.
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Understanding how resilience requirements affect compute, storage, network, and infrastructure capacity.
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Designing capacity models for disaster recovery environments, recovery objectives, and business continuity.
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Testing resilience and recovery capacity under realistic workloads and failure scenarios.
Module 15: Capacity Planning For AI, Machine Learning And Advanced Analytics
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Planning compute, storage, memory, and networking requirements for AI, machine learning, and advanced analytics workloads.
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Managing specialized resources such as GPUs and accelerated computing environments for data-intensive applications.
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Forecasting training, inference, experimentation, feature engineering, and large-scale dataset requirements.
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Balancing AI workload growth, performance targets, infrastructure utilization, and cloud expenditure.
Module 16: Advanced Performance Management And Future Directions
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Designing enterprise-wide capacity and performance management frameworks for complex and continuously evolving data platforms.
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Applying predictive analytics, intelligent monitoring, automated scaling, and machine learning for proactive capacity management.
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Managing capacity across distributed, hybrid, multi-cloud, real-time, and cloud-native data architectures.
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Emerging directions including autonomous performance optimization, intelligent resource orchestration, sustainable infrastructure, and adaptive data platforms.
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.
