Build a Complete Database Project

🔄 Quick Recall: Across seven lessons, you’ve built every database skill with AI: writing queries, designing schemas, cleaning data, optimizing performance, building reports, and implementing security. Now let’s combine everything into a complete project.

The Capstone Project

Choose one of these scenarios (or use your own):

A) E-Commerce Analytics Platform — Track orders, customers, products, and inventory. Build revenue dashboards and customer segmentation reports.

B) SaaS Application Database — Manage users, subscriptions, feature usage, and billing. Build churn prediction queries and usage reports.

C) Content Management System — Store articles, authors, categories, comments, and page views. Build editorial dashboards and content performance reports.

We’ll walk through the complete lifecycle using Scenario A. Adapt the steps to your chosen project.

Stage 1: Requirements and Schema

Start by telling AI your complete business context:

I'm building an e-commerce analytics database in PostgreSQL.

Business entities:
- Customers (individual and business accounts)
- Products (with categories and subcategories)
- Orders (with multiple items per order)
- Inventory (stock levels, reorder points)
- Reviews (ratings and text feedback)

Key queries I'll need:
- Daily/monthly revenue reports by product, category, and region
- Customer lifetime value and segmentation (RFM analysis)
- Inventory alerts (low stock, overstock)
- Product performance rankings
- Customer retention and churn analysis

Design the complete schema with:
- CREATE TABLE statements with proper types and constraints
- Foreign keys and relationship definitions
- Indexes for the key queries listed above
- Comments explaining design decisions

Review AI’s schema against the principles from Lesson 3. Check: are relationships correct? Are data types appropriate? Do the indexes match your query patterns?

Stage 2: Sample Data

Generate realistic test data:

Generate INSERT statements for my e-commerce schema with realistic data:

- 500 customers (mix of individual and business, various regions)
- 50 products across 5 categories
- 2,000 orders over the last 12 months (with seasonal patterns — higher in November/December)
- Realistic order totals ($15 - $500 range)
- Some data quality issues to test cleaning: 5% null phone numbers, a few duplicate emails

Make the data realistic: customer names from various backgrounds, product names that make sense, order patterns that reflect actual e-commerce behavior.

Stage 3: Core Queries

Build the essential queries from Lesson 2, tailored to your project:

Using my e-commerce schema, write these production-quality queries:

1. Monthly Revenue Dashboard
   - Revenue, order count, average order value by month
   - Month-over-month growth percentage
   - Dynamic dates (no hardcoded values)

2. Customer RFM Analysis
   - Recency (days since last order), Frequency (total orders), Monetary (total spending)
   - Segment customers into: Champions, Loyal, At Risk, Lost

3. Product Performance
   - Revenue, units sold, average rating per product
   - Rank within category
   - Inventory status (days of stock remaining)

4. Inventory Alerts
   - Products below reorder point
   - Predicted stockout date based on 30-day sales velocity

Requirements for all queries:
- Use CTEs for readability
- Handle NULLs and edge cases
- Include meaningful column aliases
- Execute efficiently on the sample dataset

✅ Quick Check: Why does the capstone exercise require “production-quality queries” rather than just working queries?

Because the gap between a working query and a production query is where most database problems live. A working query returns the right answer today. A production query returns the right answer every day — handling null values, zero divisions, date boundaries, and new data patterns gracefully. Building this discipline in a capstone project means you’ll write production-quality queries by default.

Stage 4: Optimization

Test and optimize your queries using Lesson 5 techniques:

1. Run EXPLAIN ANALYZE on each core query
2. Paste execution plans into AI for analysis
3. Add or adjust indexes based on recommendations
4. Rerun and compare before/after performance
5. Create materialized views for expensive dashboard queries

Target: all dashboard queries execute in under 1 second.

Stage 5: Reporting Layer

Convert your best queries into a reporting system from Lesson 6:

1. Create views for each core report
2. Create materialized views for expensive queries with a refresh schedule
3. Build the KPI query that powers dashboard summary cards
4. Add time intelligence: same period last year, rolling averages

Stage 6: Security and Production Readiness

Apply Lesson 7 practices:

1. Create roles: app_role (read/write), reporting_role (read-only), admin_role (full)
2. Grant minimum necessary permissions to each role
3. Set up a backup script (pg_dump to a local directory)
4. Create a maintenance script (VACUUM ANALYZE, bloat check)
5. Write monitoring queries (slow queries, connection count, disk usage)

Project Validation Checklist

Run through this checklist before considering the project complete:

Course Review: Your AI Database Toolkit

Here’s what you’ve built across all eight lessons:

The verification mindset ties everything together: always check AI output against known data, test on realistic volumes, and validate before production.

Key Takeaways

• A complete database project follows a clear sequence: requirements → schema → data → queries → optimization → reporting → security
• Realistic sample data (with volume and quality issues) reveals problems that empty tables hide
• Production-quality means defensive coding: null handling, edge cases, dynamic dates, and clear aliases
• The capstone validates that all skills work together — schema supports queries, queries are optimized, reports are reliable, security protects everything
• The verification mindset from Lesson 1 applies at every stage: check results, test restores, validate permissions
• Your AI database toolkit makes you dramatically faster at every phase while your judgment keeps the results trustworthy

Congratulations on completing this course. You now have a systematic approach to database work with AI — from natural language queries to production-ready systems.