Building Your First Data Pipeline: From POS to Business Intelligence

Your business runs on data scattered across a half-dozen systems. Your POS tracks sales. Your scheduling tool tracks labor. Your inventory system tracks stock. Your accounting software tracks costs. And right now, the only thing connecting them is a human being with a spreadsheet.

That human being is your bottleneck. Not because they're bad at their job — because this job shouldn't exist.

What a Data Pipeline Actually Is

A data pipeline is just automated data movement. It extracts data from one system, transforms it into a useful format, and loads it somewhere you can query it. The industry calls this ETL (Extract, Transform, Load) — but the concept is simpler than the acronym.

Think of it as replacing this workflow:

1. Log into your POS dashboard
2. Export yesterday's sales as CSV
3. Open your master spreadsheet
4. Copy-paste the new data
5. Fix the formatting
6. Update your charts
7. Email the report to your team

With this:

1. Pipeline runs automatically at 6 AM
2. Dashboard updates itself
3. Team opens it whenever they need it

Starting Simple: The Webhook Approach

The fastest way to build your first pipeline is with webhooks. Most modern POS systems, ordering platforms, and business tools can send webhooks — real-time notifications when something happens (a sale, an order, a schedule change).

Here's the basic architecture:

POS System → Webhook → Cloud Function → Database → Dashboard
              (sale happens)   (HTTP POST)    (transform)    (store)     (visualize)

A Cloud Function (Google Cloud Functions, AWS Lambda, etc.) receives the webhook, transforms the raw data into your schema, and writes it to a database. That's it. No complex infrastructure. No Kafka clusters. Just a function that catches data and puts it where it belongs.

Choosing Your Data Store

For your first pipeline, don't overthink the database choice. You need something that's easy to query and can grow with you:

• Supabase (PostgreSQL) — great for operational data you need to query in real-time. Built-in REST API, real-time subscriptions, easy to get started.
• BigQuery — great for analytical queries over large datasets. Excellent for historical analysis, very cheap storage, SQL interface.
• Both — in practice, many businesses need both. Live data in Supabase, historical analysis in BigQuery. We'll cover this in detail in another post.

The Accept-and-Queue Pattern

Here's a lesson we learned the hard way: your webhook receiver should do as little as possible. When a POS system sends a webhook, it expects a fast response. If your function takes too long processing, the POS will retry — and you'll get duplicate data.

The pattern that works:

1. Accept — receive the webhook, validate the signature, return 200 OK immediately
2. Queue — push the raw payload to a message queue (Google Pub/Sub, AWS SQS)
3. Process — a separate function reads from the queue, transforms, and loads the data

This decouples ingestion from processing. Your webhook receiver is fast and reliable. Your processing can take as long as it needs without risking timeouts or duplicates.

Monitoring From Day One

The biggest mistake in building pipelines is not monitoring them. A pipeline that silently fails is worse than no pipeline — because you trust the data that isn't there.

At minimum, monitor:

• Freshness — when did data last arrive? If it's been more than X hours, something is wrong.
• Volume — are you getting the expected number of records? A sudden drop means something broke upstream.
• Errors — any function failures, transformation errors, or write conflicts should trigger alerts.

Start Today

Your first pipeline doesn't need to be perfect. Pick your highest-value data source (usually sales), connect it to a database, and build a simple dashboard. You'll learn more in the first week of running a real pipeline than in months of planning one.