501 — Build an E-Commerce Analytics Platform

Advanced

Design, build, and query a complete e-commerce analytics database from scratch using PostgreSQL and Docker. This is a separate capstone project with its own richer schema, fresh container, and fresh dataset — not the smaller `devops_sql` schema from Lessons 402-405.

Learning Objectives

Design and create a normalized schema for a complete e-commerce system

Set up PostgreSQL via Docker and load realistic data

Write complex analytical queries using CTEs and window functions

Define result grain before aggregating so metrics stay correct

Create views for common business reports

Add indexes and verify performance with EXPLAIN ANALYZE

Implement audit triggers for data change tracking

Write monitoring queries for database health

Backup and restore the database

Step 1

Launch PostgreSQL via Docker

Spin up a fresh PostgreSQL 16 instance in a Docker container with a named volume for data persistence. This capstone intentionally uses a brand-new container and schema so you can build a production-style analytics project from scratch.

Commands to Run

docker run -d --name ecommerce-db -e POSTGRES_USER=analytics -e POSTGRES_PASSWORD=analytics123 -e POSTGRES_DB=ecommerce -p 5432:5432 -v ecommerce_pgdata:/var/lib/postgresql/data postgres:16-alpine

docker ps --filter name=ecommerce-db --format 'table {{.Names}}\t{{.Status}}\t{{.Ports}}'

docker exec -it ecommerce-db psql -U analytics -d ecommerce -c 'SELECT version();'

What This Does

We launch PostgreSQL 16 on Alpine Linux (small image) inside Docker. The -e flags set the superuser, password, and default database. Port 5432 is mapped to localhost so external tools can connect. The named volume ecommerce_pgdata ensures data survives container restarts. All subsequent commands use 'docker exec' to run psql inside this container. This capstone is intentionally separate from the `pg-sql-course` container used earlier, because the schema here includes additional analytics-friendly columns like `sku`, `cost`, `stock_qty`, `shipping_cost`, and richer review data.

Expected Outcome

Container 'ecommerce-db' running and healthy. The SELECT version() query returns something like 'PostgreSQL 16.x on ... compiled by ...'.

Pro Tips

1
If port 5432 is already in use, change -p to 5433:5432 and adjust later commands
2
Named volumes are preferred over bind mounts for database data
3
Alpine images are ~80 MB vs ~400 MB for full Debian-based images
4
You can connect GUI tools (pgAdmin, DBeaver) to localhost:5432 with user 'analytics'

Common Mistakes to Avoid

⚠️Forgetting the -d flag (container runs in foreground and blocks your terminal)
⚠️Using a bind mount path that doesn't exist (causes permission errors)
⚠️Not removing a previous container with the same name — run 'docker rm -f ecommerce-db' first if needed

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