Database Capacity Planning with Time-Series Forecasting

In today’s digital era, databases are at the heart of every application, storing and managing vast amounts of critical information. As organizations scale, predicting future database capacity becomes a crucial challenge. Without accurate forecasting, businesses risk running into performance issues, unexpected costs, or wasted resources.

Our project, Database Capacity Planning, addresses this by integrating time-series forecasting with economic indicators to predict database capacity needs up to 365 days in advance. The system provides actionable insights for proactive resource management and cost optimization.

Why Database Capacity Planning Matters

Managing database capacity is not just about storage—it directly impacts performance, uptime, and cost. Over-provisioning wastes resources and money, while under-provisioning risks downtime and degraded performance. A well-designed capacity forecasting system enables:

Proactive Scaling: Identify future demand before issues occur.

Cost Optimization: Plan budgets based on realistic projections.

Business Continuity: Avoid downtime due to sudden capacity breaches.

System Architecture

The system is designed using a modular architecture, ensuring scalability, robustness, and ease of maintenance.

1. Data Collection & Integration

Gathers internal cloud usage metrics.

Integrates external economic indicators (GDP, unemployment rates, etc.).

2. Data Preprocessing

Cleans raw data.

Handles missing values and standardizes formats.

3. Forecasting Engine

Powered by Prophet, Facebook’s open-source time-series forecasting library.

Generates predictions by analyzing historical patterns.

4. Data Access Layer

Manages database connectivity and interactions.

5. Interactive Dashboard

Built using Streamlit and Plotly for visualization.

Provides forecasts, alerts, and scenario analysis.

Forecasting Methodology

At the core of the system is the Prophet model, which analyzes historical usage data to identify patterns.

Time-Series Forecasting

Detects seasonal effects (weekly, yearly trends) and long-term growth.

Economic Indicator Integration

Uses external regressors (GDP, unemployment rates) to correlate cloud usage with broader market conditions.

Uncertainty Quantification

Provides upper and lower forecast bounds, enabling risk-aware decision-making.

Key Features

1. Predictive Analytics

365-day forecast horizon for long-term planning.

2. Real-Time Monitoring

Live visualization of current and predicted usage.

3. Configurable Alerts

Threshold-based alerts (80%, 85%, 90%).

Displays the date of first predicted breach, allowing timely scaling decisions.

4. Interactive User Experience

Streamlit-based controls to adjust forecast horizon and thresholds.

Scenario analysis for different capacity planning strategies.

User Experience

The dashboard emphasizes transparency and usability:

Time-Series Charts: Compare historical vs. predicted usage.

Alert Timeline: Visual view of upcoming capacity breaches.

Raw Data Access: Inspect the underlying dataset directly.

Technology Stack

Our project leverages a robust set of tools and libraries:

Prophet – Time-series forecasting.

SQLAlchemy – Database connectivity and ORM.

Streamlit – Interactive dashboard development.

Plotly – Dynamic data visualization.

requests – API calls to external data sources.

Data Sources:

Internal: Historical database usage logs.

External: Federal Reserve Economic Data (FRED) API for economic indicator

The Team

Saketh Dumpati (RA2211030010120)

Thrisha G (RA2211030010123)

Deva Harsha (RA2211030010149)

Conclusion

By combining historical usage data with economic insights, this project delivers a scalable and proactive database capacity planning framework. Organizations can not only prevent system failures but also optimize costs and align IT resources with business growth.

This project demonstrates how predictive analytics can transform cloud infrastructure management into a strategic advantage.