data · open source
Recommendation & Personalisation with PostgreSQL
Recommendation & Personalisation built on PostgreSQL, chosen where it genuinely fits, and swapped where it does not.
- Category
- data
- Vendor
- Open source
- Alternatives we also use
- 6
Why PostgreSQL for this
Offline accuracy and revenue are different things. We measure recommendations by experiment against the business metric, because that is the only number that pays anyone.
PostgreSQL is strongest at it handles far more workload than teams expect, with one operational model. For recommendation & personalisation that matters because the failure modes of this kind of system tend to cluster exactly there.
The honest trade-off: genuine analytical workloads past a certain scale belong in a warehouse. We say that up front because a stack chosen for fashion rather than fit becomes someone's migration project two years later. Integration comes before intelligence. A model that cannot reach your systems of record is a demo with good manners.
Six weeks to something running in production, not six quarters to a strategy document.
The honest assessment
- What it is
- Our default database, relational, JSON, full-text and vector in one engine.
- Strongest at
- it handles far more workload than teams expect, with one operational model
- Trade-off
- genuine analytical workloads past a certain scale belong in a warehouse
- Category
- data
We are not a reseller for PostgreSQL and hold no commission on this choice. Where a different option fits your workload better, the recommendation will say so. That is the entire value of asking us.
What is included
- Event tracking design, since most projects start with inadequate data
- Baseline popularity model to beat
- Hybrid collaborative and content-based ranking
- Cold-start handling for new users and new items
- A/B testing framework with proper statistics
- Business-metric reporting, not just offline accuracy
Questions
How much data do we need?
Less than people assume to start. A content-based approach works from day one; collaborative filtering improves as interaction volume grows.
How do you handle new products?
Content-based features carry new items until interaction data accumulates, with deliberate exploration so new items get a fair chance to be seen.
How do we know it is working?
Controlled A/B tests measured on revenue or engagement, with proper statistical treatment rather than eyeballing a dashboard.
Alternatives for recommendation & personalisation
Same capability, different stack. Each page states its own trade-off.
Building with PostgreSQL?
Bring us the workload and we will tell you whether this is the right stack for it.
Or email bd@dtrasglobal.com · call +91 74118 77878