Data Warehouse Tool

This tool models data warehouse sizing and cost to support design and budgeting decisions. It accepts inputs such as initial data volume, growth rate, retention window, price per TB, cloud provider, data format, and read/write mix. Users include data engineers, cloud architects, and analytics leads evaluating architectures or preparing budgets. Output focuses on capacity, cost, and basic performance implications for scenario comparison.
Conceptually, it applies deterministic sizing and cost formulas: future volume grows with the specified rate over the planning horizon; storage cost scales with volume and storage tier; retention rules determine how much data must remain online. It also yields a simple performance signal to aid design comparisons without full benchmarks.
Core features include volume projection, tier-aware cost estimation, retention impact, partitioning guidance, and scenario export. Required inputs cover data volume, growth, retention, price, provider, and tier; optional inputs enable advanced pricing models and data organization patterns. Outputs include projected volume, monthly and yearly costs, partitioning recommendations, and a simple performance readiness score.
Common use cases include building budgeting forecasts, evaluating cloud pricing, and testing design decisions before implementation. The tool helps teams communicate tradeoffs to stakeholders and align technical design with financial constraints. Its unique value lies in explicit, repeatable modeling with clear assumptions and easy scenario comparison.