The Power of Unified Data
The single-database architecture enables queries that span business units. This is impossible with separate databases without complex ETL pipelines.
Example 1: Shopify Products Across All Apps
One shopify_products table serves every surface:
-- Jarvis: "Show me my top selling products"
SELECT name, total_sales, inventory_count
FROM shopify_products
ORDER BY total_sales DESC
LIMIT 5;
-- Publisher: Travel blog sidebar widget
SELECT name, image_url, price, affiliate_url
FROM shopify_products
WHERE vertical = 'travel' AND in_stock = true;
-- Agency: Client performance dashboard
SELECT
ac.company_name,
COUNT(so.id) as orders,
SUM(so.total) as revenue
FROM agency_clients ac
JOIN shopify_orders so ON so.client_id = ac.id
GROUP BY ac.id;
Example 2: Affiliate Revenue Attribution
Track which content drives affiliate sales:
-- Which blog posts drive Viator bookings?
SELECT
pp.title as article,
pv.name as vertical,
COUNT(vc.id) as clicks,
SUM(vc.commission_earned) as revenue
FROM publisher_posts pp
JOIN publisher_verticals pv ON pp.vertical_id = pv.id
JOIN publisher_post_viator_products ppvp ON ppvp.post_id = pp.id
JOIN viator_click_tracking vc ON vc.product_id = ppvp.viator_product_id
WHERE vc.converted = true
GROUP BY pp.id, pv.id
ORDER BY revenue DESC;
Example 3: Creator-Product Matching
Find creators who could promote specific products:
-- Match creators to products by vertical alignment
SELECT
cp.display_name as creator,
cp.audience_size,
sp.name as product,
sp.commission_rate
FROM creator_profiles cp
JOIN content_analysis ca ON ca.creator_id = cp.id
JOIN shopify_products sp ON sp.vertical = ca.primary_vertical
WHERE cp.verified = true
AND sp.in_stock = true
AND ca.engagement_rate > 0.03;
Example 4: Unified User Intelligence
Single user record with activity across all products:
-- Full user journey: visitor → reader → customer → creator
SELECT
u.email,
COUNT(DISTINCT ae.id) as page_views,
COUNT(DISTINCT so.id) as purchases,
SUM(so.total) as lifetime_value,
cp.creator_tier,
SUM(clp.amount) as licensing_revenue
FROM users u
LEFT JOIN audience_events ae ON ae.user_id = u.id
LEFT JOIN shopify_orders so ON so.customer_email = u.email
LEFT JOIN creator_profiles cp ON cp.user_id = u.id
LEFT JOIN creator_license_purchases clp ON clp.creator_id = cp.id
GROUP BY u.id, cp.creator_tier;
Example 5: Agency Benchmarking
Compare client performance against internal Publisher metrics:
-- How do agency clients compare to our own verticals?
WITH our_performance AS (
SELECT
'Internal' as source,
pv.name as vertical,
AVG(ppa.page_views) as avg_views,
AVG(ppa.conversion_rate) as avg_conversion
FROM publisher_verticals pv
JOIN publisher_posts pp ON pp.vertical_id = pv.id
JOIN publisher_post_analytics ppa ON ppa.post_id = pp.id
GROUP BY pv.id
),
client_performance AS (
SELECT
'Client: ' || ac.company_name as source,
ad.vertical,
AVG(ad.page_views) as avg_views,
AVG(ad.conversion_rate) as avg_conversion
FROM agency_clients ac
JOIN agency_deliverables ad ON ad.client_id = ac.id
GROUP BY ac.id, ad.vertical
)
SELECT * FROM our_performance
UNION ALL
SELECT * FROM client_performance
ORDER BY vertical, source;
Example 6: Jarvis Full Context
Voice assistant with complete business intelligence:
-- Jarvis query: "How's the business doing this week?"
SELECT
'Publisher' as unit,
COUNT(*) as activity,
'posts published' as metric
FROM publisher_posts
WHERE created_at > NOW() - INTERVAL '7 days'
UNION ALL
SELECT
'Store',
COUNT(*),
'orders'
FROM shopify_orders
WHERE created_at > NOW() - INTERVAL '7 days'
UNION ALL
SELECT
'Platform',
COUNT(*),
'licenses sold'
FROM creator_license_purchases
WHERE created_at > NOW() - INTERVAL '7 days'
UNION ALL
SELECT
'Agency',
COUNT(*),
'deliverables completed'
FROM agency_deliverables
WHERE completed_at > NOW() - INTERVAL '7 days';
Why This Matters
| Separate Databases | Single Database |
|---|
| ETL pipelines to sync data | Direct JOINs |
| Stale data (sync delays) | Real-time queries |
| Multiple user accounts | One identity everywhere |
| AI sees partial picture | AI has full context |
| Complex maintenance | One schema to manage |
The compounding effect: Every new table added benefits all products. Add influencer_campaigns once, and Publisher, Platform, Agency, Store, and Jarvis can all query it immediately.