Description

This curriculum spans the technical and operational rigor of a multi-workshop capacity planning engagement, covering the same depth of analysis and cross-system coordination required for enterprise database infrastructure reviews, from storage architecture and performance baselines to cloud-native scaling and compliance-driven governance.

Module 1: Defining Database Capacity Requirements in Enterprise Systems

Select capacity thresholds for OLTP workloads based on transaction volume projections and peak concurrency demands.
Size database instances using historical growth trends in data volume, accounting for retention policies and archival strategies.
Allocate memory and CPU resources considering query complexity, indexing overhead, and background maintenance tasks.
Define acceptable response time SLAs for critical queries and align instance sizing to meet latency targets under load.
Assess the impact of row-level security and data masking on query performance during capacity modeling.
Integrate application release cycles into capacity planning to anticipate schema changes and index rebuild requirements.
Model capacity needs for sharded versus monolithic database architectures based on data distribution patterns.
Coordinate with application teams to quantify batch job footprints and their effect on daily load profiles.

Module 2: Storage Architecture and I/O Performance Optimization

Select storage tier (SSD, NVMe, HDD) based on IOPS requirements, durability needs, and cost per GB for specific workloads.
Configure RAID levels and filesystem block sizes to balance redundancy, throughput, and random access performance.
Implement partitioning strategies that align with query access patterns to reduce I/O load on hot partitions.
Monitor and tune disk queue depth to prevent saturation under concurrent write-heavy operations.
Size transaction log volumes based on redo generation rates during peak batch processing windows.
Plan for storage auto-scaling policies while setting upper limits to prevent runaway provisioning costs.
Configure direct I/O and disable filesystem caching where database engines manage their own buffer pools.
Validate storage path redundancy and failover behavior in clustered database environments.

Module 3: Capacity Modeling for High Availability and Disaster Recovery

Size standby database instances to support read scaling without degrading failover readiness.
Calculate log shipping or replication bandwidth needs across geodistributed data centers.
Size redo transport queues to handle network latency spikes without replication lag.
Allocate additional buffer pool memory on standby systems if read-only queries are enabled.
Model RPO and RTO requirements against replication method (synchronous vs. asynchronous) and network constraints.
Size backup storage capacity to accommodate compressed and uncompressed copies across retention periods.
Plan failover testing windows that do not exceed available standby capacity headroom.
Account for increased redo generation during index rebuilds or bulk loads in replication capacity models.

Module 4: Workload Characterization and Performance Baselines

Classify workloads into categories (OLTP, DSS, ETL) and assign distinct capacity profiles.
Instrument query execution statistics to identify top resource-consuming statements for optimization.
Establish baseline CPU, memory, and I/O utilization during normal operations for anomaly detection.
Map long-running queries to specific time windows and allocate headroom during those periods.
Use wait event analysis to distinguish between CPU-bound, I/O-bound, and lock contention scenarios.
Correlate application release events with performance regressions in capacity telemetry.
Define workload replay procedures to simulate production load on scaled-down test environments.
Tag database sessions by application module to attribute resource usage accurately.

Module 5: Scaling Strategies: Vertical, Horizontal, and Elastic

Determine vertical scaling limits based on hypervisor constraints and OS memory addressing.
Design connection pooling strategies that scale effectively with read replicas and application instances.
Implement sharding key selection that minimizes cross-shard queries and rebalancing overhead.
Configure auto-scaling policies using metrics such as active sessions, CPU utilization, and queue depth.
Set cooldown periods in auto-scaling groups to prevent flapping during transient load spikes.
Validate query plan stability when scaling out to prevent performance degradation due to distributed joins.
Assess licensing implications of dynamic scaling in commercial database platforms.
Pre-size buffer pools and sort areas on new nodes to avoid cold-start performance issues.

Module 6: Monitoring, Alerting, and Capacity Forecasting

Define capacity thresholds for alerting that balance sensitivity with operational noise.
Implement time-series forecasting models using seasonal decomposition to predict storage growth.
Configure monitoring agents to sample performance counters without introducing overhead.
Integrate capacity metrics into centralized observability platforms with standardized tagging.
Set up early warning alerts for filesystems approaching 80% utilization to allow remediation time.
Track index bloat and table fragmentation as leading indicators of performance degradation.
Correlate database locks and latch waits with CPU saturation events in alert correlation rules.
Automate capacity reports for infrastructure review boards using templated dashboards.

Module 7: Capacity Impacts of Database Maintenance Operations

Schedule index rebuilds during maintenance windows with sufficient I/O and CPU headroom.
Estimate temporary space requirements for large sort and hash operations during vacuum operations.
Size maintenance windows based on table growth rates and fragmentation thresholds.
Allocate additional redo log space during bulk data loads to prevent log switch stalls.
Plan for increased memory pressure during statistics gathering on large partitioned tables.
Coordinate maintenance tasks across clustered instances to avoid resource contention.
Pre-size temporary tablespaces based on peak ETL job requirements.
Monitor archive log generation during maintenance and adjust retention policies accordingly.

Module 8: Cloud-Native Database Capacity Management

Select provisioned versus serverless database tiers based on workload predictability and cost sensitivity.
Configure storage auto-growth policies with upper bounds to prevent cost overruns.
Monitor and optimize connection limits in managed database services with fixed session caps.
Size cloud-native backup storage considering cross-region replication and retention.
Plan for cold start delays in serverless databases during sudden traffic spikes.
Track data egress costs in multi-cloud architectures and factor into capacity decisions.
Implement tagging policies for cloud databases to enable chargeback and showback reporting.
Validate performance isolation guarantees in shared-tenant cloud database offerings.

Module 9: Governance, Compliance, and Cross-Team Coordination

Enforce capacity review gates in change management processes for schema and index changes.
Define ownership roles for capacity planning between DBAs, cloud teams, and application owners.
Document capacity assumptions for audit purposes, including growth rates and SLA targets.
Establish data retention policies that directly influence storage capacity planning.
Coordinate capacity requests with procurement cycles for on-premises infrastructure.
Implement chargeback models that incentivize efficient database resource usage.
Review capacity plans against data sovereignty requirements affecting regional deployments.
Conduct quarterly capacity alignment sessions with application stakeholders to revise forecasts.