← Back to portfolio Product Analytics + A/B on Supabase preview
Stack
PythonStreamlitSupabase (Postgres + RLS + Edge Functions)SQLsupabase-py
Impact
  • Full-stack: Streamlit UI + Supabase Postgres + Edge Function ingest (API-key auth)
  • Row Level Security on every table — a user sees only their org's rows
  • SQL views compute funnel, cohort, MRR, DAU, channel conversion — the dashboard reshapes, it does not aggregate
  • A/B result in the DB: control 32.1% vs treatment 37.2%, p = 0.0034 (chi-square)
Source View on GitHub → Repo is private — available on request

Product Analytics + A/B on Supabase

Business Context

Most analytics portfolios show metrics on a clean CSV. This project closes a different gap — how analytics embeds into a real multi-tenant product: auth, per-org data isolation, an event ingest path, and an experiment whose result is computed in the database, not in a notebook.

Architecture

Client/seed  ── POST /functions/v1/ingest (x-api-key) ──►  Edge Function (Deno)
                                                              │
Streamlit    ◄── email/password auth + SQL (RLS-scoped) ──►  Supabase Postgres
dashboard        (supabase-py, anon key)                      analytics + experiments
  • Schemas: analytics (organizations, api_keys, users, events, subscriptions, org_members) and experiments (experiments, variants, assignments, metrics).
  • Analytics in the DB: SQL views compute funnel, cohort retention, MRR, DAU, and channel conversion (sql/002_views.sql) — the dashboard reshapes, it does not aggregate.
  • A/B in the DB: experiments.v_results computes per-variant assigned/converted/conversion; the chi-square test runs on top in the dashboard.
  • Ingest: the Edge Function validates an API key (SHA-256 hash, never plaintext) and inserts the event via a security definer function.

Security model (RLS)

Every table has Row Level Security enabled. A user only ever sees rows of their organization — the dashboard is safe to expose to real users, not just to run locally.

Insight

The key point: analytics is computed where the data lives. SQL views and v_results mean metrics and experiment results are consistent across any client that connects to the database — dashboard, BI tool, or ad-hoc SQL all see the same numbers.

Impact

  • Full-stack path — instrument → ingest → isolate → analyze → experiment in one repo.
  • Concluded A/B — control 32.1% vs treatment 37.2%, p = 0.0034 (chi-square), +5.1pp lift.
  • RLS on every table — the dashboard is safe to expose to real users.
  • Reused UI — the presentation layer is taken from streamlit-app; only the data layer changed.

Documentation

Case study

Problem

A dashboard on synthetic data proves analytics skills, but it does not show how analytics lives inside a real multi-tenant product: auth, per-tenant data isolation, an ingest path, and experiment results computed where the data lives.

Approach

Reused the UI from streamlit-app but swapped the data layer from an in-memory generator to Supabase Postgres. RLS is enabled on every table so a user sees only their org's rows; an Edge Function validates an API key (stored as SHA-256) and inserts events via a security-definer function. Analytics live in SQL views (funnel, cohort, MRR, DAU, channel conversion) and the A/B result is computed in the DB (v_results) with the chi-square test run on top.

Result

One repo demonstrates the full path: instrument → ingest → isolate → analyze → experiment. The concluded A/B shows control 32.1% vs treatment 37.2% (p = 0.0034), and the security model means the dashboard is safe to expose to real users, not just to run locally.

+5.1pp A/B lift
0.0034 p-value
all tables RLS isolation
Edge Function Ingest