Course Introduction
The Database Performance Engineering and Capacity Optimization Training Course provides a comprehensive framework for diagnosing, engineering, optimizing and continuously managing database performance across enterprise environments. The programme examines the relationship between database architecture, workload behaviour, SQL execution, storage, compute resources, concurrency, configuration and application design, enabling participants to establish systematic approaches for improving responsiveness, throughput, scalability and operational efficiency.
The course develops practical expertise in understanding database performance as an end-to-end engineering discipline rather than a narrow exercise in query tuning. Participants explore performance baselining, workload characterization, bottleneck identification, execution-plan analysis, indexing, partitioning, statistics, caching, resource allocation and database configuration. Particular attention is given to distinguishing symptoms from root causes and applying evidence-based optimization techniques that improve performance without introducing unnecessary complexity or compromising reliability.
Participants will examine methods for measuring and managing database capacity across transactional, analytical and mixed workloads. The programme covers capacity forecasting, utilization analysis, growth modelling, concurrency assessment, resource planning and workload management. Participants learn how to determine when performance problems are caused by insufficient resources, inefficient queries, poor data structures, application behaviour, contention, infrastructure limitations or architectural constraints, and how to establish appropriate remediation and scaling strategies.
The programme addresses performance optimization across modern database architectures, including traditional enterprise databases, data warehouses, cloud databases, distributed databases, database-as-a-service platforms and hybrid environments. Participants examine vertical and horizontal scaling, read replicas, sharding, clustering, partitioning, caching and workload isolation. The course also explores the performance implications of cloud elasticity, consumption-based infrastructure, serverless databases and distributed processing, including the need to balance performance improvements with operational and financial efficiency.
Emerging technologies and issues are integrated throughout the course, including database observability, automated performance tuning, AI-assisted database administration, intelligent workload management, real-time analytics, cloud-native database services and increasingly distributed data architectures. Participants consider how artificial intelligence and automation can assist with anomaly detection, capacity forecasting, query optimization and operational decision-making while maintaining human oversight, governance, security and performance assurance.
Through practical performance analysis, execution-plan interpretation, capacity modelling, workload optimization and troubleshooting exercises, participants will develop a repeatable methodology for engineering database performance. By the end of the course, participants will be better prepared to establish performance baselines, diagnose bottlenecks, optimize database workloads, forecast capacity requirements, control resource consumption and create proactive performance-management practices that support reliable and scalable enterprise data services.
Duration
10 days
Who Should Attend
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Database administrators responsible for database performance, capacity, availability and operational optimization.
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Database performance engineers diagnosing complex performance and scalability problems across enterprise environments.
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Database architects designing high-performing and scalable database architectures.
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Data architects evaluating database structures, workloads and platform performance requirements.
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Cloud database engineers managing performance across cloud-native and managed database services.
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Infrastructure engineers responsible for compute, memory, storage, network and virtualization resources supporting databases.
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Data engineers optimizing database workloads, data pipelines and transformation processes.
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Application architects assessing application-database interactions and performance dependencies.
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Software engineers responsible for SQL development, database access patterns and application performance.
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Data warehouse professionals optimizing analytical queries, workloads and large-scale data processing.
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Database reliability and operations engineers responsible for database stability, observability and incident management.
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Capacity planning professionals forecasting infrastructure and database resource requirements.
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IT managers overseeing database performance improvement, modernization and infrastructure optimization programmes.
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Cloud architects balancing scalability, performance, availability and consumption costs for database platforms.
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Performance testing and quality assurance professionals validating database behaviour under varying workloads.
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Information technology service managers responsible for database service levels and operational performance.
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Technology consultants advising organizations on database tuning, scalability, capacity and performance engineering.
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Project and programme managers leading database optimization, modernization or platform transformation initiatives.
Course Objectives
By the end of this course, participants will be able to:
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Develop systematic database performance engineering frameworks that connect business service requirements with workload behaviour, technical metrics and measurable performance objectives.
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Establish meaningful performance baselines using response time, throughput, concurrency, resource utilization and workload characteristics to identify deviations and optimization opportunities.
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Analyse database execution plans and workload patterns to identify inefficient queries, costly operations, contention points, resource bottlenecks and architectural limitations.
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Apply advanced SQL optimization techniques involving joins, predicates, aggregations, indexes, statistics, execution strategies and data-access patterns across diverse database workloads.
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Design appropriate indexing, partitioning, clustering and physical data structures that improve performance while balancing storage overhead and maintenance requirements.
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Evaluate CPU, memory, storage, network and I/O utilization to determine resource constraints and distinguish infrastructure limitations from database or application-level problems.
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Develop capacity models that forecast data growth, transaction volumes, concurrent users, workload changes and infrastructure requirements over defined planning horizons.
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Apply workload management techniques to control concurrency, prioritize critical workloads, isolate resource-intensive processes and maintain predictable database service levels.
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Evaluate scaling strategies including vertical scaling, horizontal scaling, replication, sharding, caching and distributed processing according to workload and architectural requirements.
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Establish database observability and proactive monitoring practices that detect performance degradation, anomalies, capacity constraints and emerging operational risks before they affect users.
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Assess cloud database performance and cost trade-offs involving elastic resources, consumption-based services, managed platforms, workload scaling and infrastructure optimization.
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Create sustainable database performance and capacity-management strategies incorporating automation, continuous improvement, governance, resilience, security and evolving enterprise technology requirements.
Comprehensive Course Outline
Module 1: Foundations of Database Performance Engineering
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Principles of database performance engineering and the relationship between performance, availability, scalability, reliability, user experience and business service outcomes.
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Key database performance metrics including latency, throughput, transactions per second, concurrency, utilization, queueing, wait events and resource consumption.
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Differences between transactional, analytical, batch, mixed and distributed workloads and the performance characteristics associated with each workload type.
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Common causes of database performance degradation, including inefficient SQL, poor data modelling, contention, configuration problems, resource shortages and application behaviour.
Module 2: Performance Requirements, Service Levels and Baselines
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Techniques for translating business expectations into measurable database performance requirements covering response times, throughput, concurrency and service availability.
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Development of performance baselines that establish normal workload behaviour and provide reference points for identifying degradation and optimization opportunities.
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Definition of service-level indicators and objectives for critical database services, applications, analytical workloads and business processes.
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Performance benchmarking approaches for comparing database configurations, platforms, workloads and optimization changes using controlled and repeatable measurements.
Module 3: Database Workload Characterization and Profiling
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Workload profiling techniques for understanding transaction volumes, query patterns, concurrency, execution frequency, data access behaviour and resource consumption.
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Identification of peak, average, seasonal and exceptional workloads to support effective performance engineering and capacity planning decisions.
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Analysis of workload mixes involving online transactions, reporting, analytics, batch processing, integration and background jobs sharing common database resources.
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Development of workload classification models that support prioritization, isolation, resource allocation and targeted performance optimization.
Module 4: SQL Performance Analysis and Query Optimization
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Analysis of SQL statements to identify inefficient joins, excessive data access, unnecessary calculations, poorly selective predicates and expensive aggregation operations.
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Execution-plan interpretation techniques for understanding access paths, join algorithms, cardinality estimates, sorting, filtering, scans and other database operations.
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Query optimization strategies involving rewritten SQL, appropriate predicates, optimized joins, selective access paths and workload-aware query design.
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Techniques for measuring query improvements and ensuring optimization changes produce consistent benefits without creating adverse effects on other workloads.
Module 5: Indexing, Partitioning and Physical Design
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Index design principles for transactional and analytical databases, including selectivity, access patterns, maintenance overhead, storage consumption and workload impact.
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Partitioning strategies based on time, ranges, keys and workload characteristics to improve query performance, maintenance operations and data lifecycle management.
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Clustering and data-locality techniques for reducing unnecessary data movement and improving access efficiency in large database environments.
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Evaluation of physical design changes using controlled testing, workload analysis and performance metrics to determine whether the expected benefits justify operational costs.
Module 6: Database Configuration and Resource Optimization
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Database configuration principles involving memory allocation, connection management, caching, parallelism, background processes and workload-specific settings.
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Analysis of CPU, memory, storage and I/O behaviour to identify resource constraints and determine appropriate configuration or infrastructure changes.
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Techniques for tuning database resources without creating instability, resource starvation, excessive contention or unpredictable behaviour across competing workloads.
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Configuration governance practices for documenting, testing, approving and continuously reviewing performance-related database settings.
Module 7: Concurrency, Locking and Contention Management
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Database concurrency concepts involving transactions, isolation levels, locking, blocking, deadlocks and competing access to shared resources.
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Identification and diagnosis of blocking chains, lock contention, long-running transactions and concurrency patterns that negatively affect application responsiveness.
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Techniques for reducing contention through transaction design, indexing, workload separation, isolation strategies and application-database coordination.
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Monitoring and prevention practices for maintaining predictable performance in high-concurrency transactional environments.
Module 8: Capacity Planning and Forecasting
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Principles of database capacity management covering compute, memory, storage, I/O, network, connections, transaction volumes and concurrent workload requirements.
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Data growth modelling techniques for forecasting database size, storage consumption, index growth, backup requirements and processing demand.
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Capacity forecasting using historical trends, business growth assumptions, seasonal behaviour and expected changes in applications or analytical workloads.
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Development of capacity thresholds, alerts and escalation processes that trigger proactive infrastructure or workload optimization before service degradation occurs.
Module 9: Scaling and High-Performance Database Architectures
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Vertical and horizontal scaling approaches and the conditions under which additional compute, memory or distributed resources can effectively improve database performance.
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Replication, read replicas and workload routing techniques for distributing database demand while maintaining appropriate consistency and operational control.
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Sharding, clustering and distributed database strategies for supporting very large datasets, high transaction volumes and geographically distributed workloads.
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Evaluation of scaling architectures based on latency, consistency, availability, complexity, operational skills, resilience and total cost of ownership.
Module 10: Cloud Database Performance and Optimization
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Performance engineering considerations for cloud databases involving elastic compute, managed services, storage architectures, network latency and consumption-based resources.
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Cloud scaling strategies covering autoscaling, resource resizing, workload scheduling, serverless processing and workload-specific infrastructure allocation.
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Optimization of cloud storage and compute consumption while maintaining performance targets and avoiding unnecessary resource provisioning.
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Performance and cost governance practices for cloud databases, including workload tagging, usage monitoring, optimization cycles and FinOps collaboration.
Module 11: Data Warehouse and Analytical Workload Performance
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Performance optimization principles for analytical databases involving large scans, aggregations, joins, dimensional models, materialized structures and high-volume reporting.
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Techniques for improving data warehouse workloads through partitioning, clustering, compression, aggregation, caching and workload-aware physical design.
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Management of competing analytical workloads involving dashboards, scheduled reporting, ad hoc queries, data transformation and data science processing.
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Evaluation of emerging analytical architectures including cloud warehouses, lakehouses and distributed query engines from a performance and capacity perspective.
Module 12: Monitoring, Observability and Performance Diagnostics
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Design of comprehensive database monitoring frameworks covering response time, throughput, resource usage, wait events, workload behaviour and system health.
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Database observability practices for connecting infrastructure metrics, database events, query performance, application behaviour and user-facing service indicators.
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Root-cause analysis methods for tracing performance incidents across databases, applications, networks, storage platforms and integrated data services.
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Development of performance dashboards, alerts and diagnostic workflows that support proactive detection, rapid troubleshooting and continuous optimization.
Module 13: Performance Testing and Troubleshooting
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Development of performance testing strategies covering load, stress, endurance, concurrency, scalability and failover scenarios for database-dependent applications.
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Workload simulation techniques for reproducing production-like conditions and identifying performance limits before systems are released or significantly changed.
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Structured troubleshooting methods for separating symptoms from root causes and prioritizing corrective actions based on measurable evidence.
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Post-incident performance analysis covering contributing factors, remediation effectiveness, lessons learned and preventive improvements to reduce recurrence.
Module 14: Automation and Intelligent Performance Management
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Automation opportunities for performance monitoring, configuration management, index analysis, capacity alerts, maintenance and recurring database optimization activities.
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Application of infrastructure-as-code and configuration automation to establish consistent, repeatable and auditable database performance environments.
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Emerging use of artificial intelligence and machine learning for anomaly detection, workload forecasting, query recommendations, automated tuning and predictive capacity management.
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Governance considerations for AI-assisted performance optimization, including validation, human oversight, explainability, change control and operational safety.
Module 15: Performance Governance, Reliability and Cost Optimization
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Development of performance governance frameworks covering ownership, standards, service levels, escalation procedures, optimization responsibilities and reporting.
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Integration of performance engineering with database reliability practices involving availability, backup, recovery, resilience and disaster recovery requirements.
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Identification of performance-related technical debt and development of structured remediation programmes for inefficient architectures, obsolete configurations and poorly optimized workloads.
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Balancing performance improvements with licensing, infrastructure, cloud consumption, operational effort and total cost of ownership to achieve sustainable optimization.
Module 16: Enterprise Performance Engineering Strategy and Capstone
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Development of enterprise database performance strategies covering baselines, monitoring, workload management, optimization, capacity planning and continuous improvement.
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Creation of performance improvement roadmaps that prioritize critical workloads, high-impact bottlenecks, capacity risks, architectural constraints and measurable business outcomes.
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Integration of observability, automation, cloud optimization, intelligent performance management and proactive capacity forecasting into modern database operating models.
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Capstone exercise involving diagnosis of a complex database performance environment, development of a capacity model and creation of an actionable optimization and governance strategy.
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.
