Description

This curriculum spans the technical, governance, and operational practices of data capacity management found in multi-workshop organizational programs, covering infrastructure assessment, forecasting, tiering, lifecycle controls, distributed systems design, and cross-functional alignment as practiced in enterprise data platform teams.

Module 1: Assessing Current Data Infrastructure Capacity

Conduct inventory audits of on-premises storage arrays, cloud buckets, and data lake zones to quantify usable versus allocated capacity.
Evaluate I/O throughput bottlenecks in existing data pipelines by analyzing disk utilization and network saturation during peak ETL windows.
Map data lifecycle stages across systems to identify redundant or stale datasets consuming active storage resources.
Measure growth rates of structured and unstructured data sources over trailing 12-month periods to project near-term capacity needs.
Compare compression ratios across file formats (Parquet, ORC, Avro) in production workloads to assess storage efficiency trade-offs.
Integrate monitoring tools (e.g., Prometheus, CloudWatch) with storage layers to establish baseline utilization metrics for capacity planning.
Identify shadow IT data stores deployed outside central governance that contribute to unmanaged capacity consumption.
Document SLA requirements for data availability and access latency to determine appropriate storage tiers.

Module 2: Forecasting Data Growth and Demand Patterns

Develop time-series models using historical ingestion rates to project storage demand under multiple business growth scenarios.
Incorporate product roadmap inputs (e.g., new sensor deployments, customer acquisition targets) into data volume projections.
Adjust forecasts based on data retention policy changes, such as extending compliance holds for regulatory requirements.
Factor in seasonal data spikes (e.g., fiscal year-end reporting, holiday transaction surges) when sizing infrastructure.
Model the impact of new data sources (e.g., IoT streams, clickstream logs) on storage and processing capacity.
Validate forecast assumptions with departmental stakeholders to align technical capacity with business initiatives.
Quantify the storage implications of increasing data resolution (e.g., moving from hourly to minute-level aggregation).
Assess the effect of data replication across regions on total storage footprint and network bandwidth.

Module 3: Storage Tiering and Cost-Performance Optimization

Define policies for automated data migration between hot, warm, and cold storage tiers based on access frequency.
Implement lifecycle rules in object storage (e.g., S3 Glacier, Azure Archive) to enforce cost-effective data aging.
Evaluate trade-offs between query performance and storage cost when selecting file partitioning strategies.
Configure caching layers (e.g., Redis, Alluxio) to reduce repeated reads from high-latency storage systems.
Right-size compute-storage pairings in cloud data warehouses to avoid over-provisioning (e.g., Redshift RA3 nodes).
Negotiate reserved capacity or volume discounts with cloud providers based on committed usage forecasts.
Monitor and enforce tagging policies to allocate storage costs accurately across business units.
Assess the total cost of ownership for on-premises versus cloud storage, including power, cooling, and maintenance.

Module 4: Data Lifecycle Management and Retention Policies

Implement automated data purging workflows for datasets exceeding regulatory or operational retention periods.
Design audit trails for data deletion activities to support compliance with GDPR, CCPA, and HIPAA.
Coordinate legal holds with data engineering teams to suspend automated deletion during litigation.
Classify data assets by sensitivity and business criticality to determine appropriate retention durations.
Integrate data catalog tools with retention policies to provide visibility into expiration timelines.
Enforce immutable logging for critical datasets using write-once-read-many (WORM) storage configurations.
Balance data minimization principles with analytical needs for historical trend analysis.
Update retention policies in response to changing regulatory requirements or internal data governance standards.

Module 5: Scalability Architecture for Distributed Data Systems

Design sharded database topologies to distribute data load and avoid single-node capacity limits.
Configure auto-scaling policies for cloud data platforms (e.g., BigQuery, Snowflake) based on query concurrency and data volume.
Implement data compaction routines in distributed file systems (e.g., HDFS, Delta Lake) to reduce small file overhead.
Size Kafka cluster partitions and replication factors to handle message throughput without disk saturation.
Optimize data placement across availability zones to maintain performance during node failures.
Plan for metadata scalability in data lakes by managing file count limits in object storage directories.
Use zone-relocation strategies in cloud storage to align data proximity with compute workloads.
Test failover mechanisms under high data ingestion loads to validate system resilience.

Module 6: Data Compression and Encoding Strategies

Select columnar compression codecs (e.g., Zstandard, Snappy) based on CPU overhead and compression ratio benchmarks.
Compare dictionary encoding effectiveness for high-cardinality categorical fields in analytical tables.
Implement data deduplication at ingestion to prevent redundant record storage.
Adjust compression settings during batch loads to balance write performance and storage savings.
Monitor decompression latency in query execution plans to identify performance bottlenecks.
Apply tiered compression: aggressive for archival data, lighter for frequently accessed datasets.
Validate data integrity after compression/decompression cycles using checksum verification.
Standardize encoding formats (UTF-8, ISO-8859-1) to prevent storage bloat from mixed character sets.

Module 7: Governance and Capacity Accountability Frameworks

Establish data ownership roles with accountability for storage usage and lifecycle management.
Implement chargeback or showback models to allocate storage costs to consuming teams.
Set quotas on user or project-level storage allocations in shared data platforms.
Conduct quarterly data stewardship reviews to validate continued business value of stored datasets.
Integrate capacity alerts with incident management systems to trigger governance reviews.
Define escalation paths for capacity overruns requiring infrastructure investment approval.
Enforce schema evolution policies to prevent uncontrolled growth from unmanaged field additions.
Audit access patterns to identify orphaned datasets no longer used by active workflows.

Module 8: Monitoring, Alerting, and Capacity Drift Management

Deploy predictive alerting models that trigger warnings before storage utilization reaches critical thresholds.
Correlate capacity trends with business KPIs to distinguish expected growth from anomalous usage.
Configure automated reporting of top storage-consuming datasets for executive review.
Integrate capacity metrics into runbooks for incident response and root cause analysis.
Track variance between forecasted and actual usage to refine future capacity models.
Set up anomaly detection on ingestion pipelines to catch runaway data generation early.
Standardize alert severity levels based on remaining runway (e.g., 30, 15, 7 days of capacity left).
Validate backup and replication storage requirements in disaster recovery capacity planning.

Module 9: Cross-Functional Alignment and Change Management

Facilitate capacity planning workshops with engineering, finance, and legal teams to align on constraints.
Document technical trade-offs when enforcing capacity limits on high-priority business initiatives.
Coordinate data migration timelines during infrastructure upgrades to minimize service disruption.
Negotiate phased rollouts for storage policy changes to allow teams time for adjustment.
Communicate upcoming capacity constraints to application teams to influence data design decisions.
Integrate capacity impact assessments into the change advisory board (CAB) review process.
Manage stakeholder expectations when enforcing data deletion or access restrictions for capacity reasons.
Update runbooks and operational procedures following changes to storage architecture or policies.