A developer published a Medium article describing how they built a custom BigQuery event tracker in two weekends using ChatGPT. They were right—it works. What they didn’t mention is the 4-8 hours per month of maintenance that production pipelines require indefinitely. The prototype is the easy part. The question isn’t whether you can build a WordPress-to-BigQuery pipeline. The question is whether you should maintain one.

The DIY Architecture That Actually Works#

The architecture described in the Medium article is sound. WordPress fires a hook on every meaningful action—page views, add-to-cart, purchases. A lightweight function captures that event, formats it as JSON, and sends it to a Google Cloud Run endpoint. Cloud Run processes the payload and either inserts directly into BigQuery via the Streaming Insert API or publishes to a Pub/Sub topic that automatically delivers to BigQuery.

Google Cloud Run’s free tier includes 2 million requests per month and 360,000 vCPU-seconds—more than enough for most WooCommerce stores (Google Cloud Run Pricing, 2025). Your BigQuery costs for Streaming Insert sit at $0.01 per 200MB. For a store doing 500 orders per month, you’re looking at pennies in infrastructure costs.

The flow looks like this:

1. WordPress + WooCommerce hook fires (add_to_cart, purchase, page_view)
2. Custom PHP function captures event data and sends HTTP POST to Cloud Run
3. Cloud Run endpoint validates, formats, and inserts into BigQuery
4. Data becomes queryable within seconds

That’s elegant. That’s real. And for a weekend project, it’s impressive.

You may be interested in: WooCommerce Events to BigQuery Without GA4: The Direct Pipeline Guide

What the Medium Article Got Right#

Credit where it’s due. The article demonstrates three things that matter:

First, GA4 is not the only path to BigQuery. You don’t need GA4 as a middleman to get WordPress events into your data warehouse. Direct streaming is faster, cleaner, and gives you full control over your schema.

Second, Google Cloud infrastructure costs are genuinely low. Cloud Run’s free tier is generous. Pub/Sub charges $0.04 per 100,000 messages. BigQuery’s first 1TB of querying per month is free. For small-to-medium WooCommerce stores, infrastructure costs stay under $10/month.

Third, ChatGPT can scaffold the boilerplate. Authentication, API formatting, basic error responses—AI code assistants handle the repetitive parts well. The prototype comes together fast.

What Gets Glossed Over: The Production Gap#

Here’s where weekend projects diverge from production systems. Production event pipelines require error handling, retry logic, dead letter queues, monitoring, and alerting—adding 5-10x development time beyond the initial prototype (data engineering production readiness analysis, 2025).

The Medium article describes the happy path. Events fire, Cloud Run receives them, BigQuery ingests them. But production means handling every unhappy path:

Authentication and Security

Your Cloud Run endpoint is a public URL. Without proper authentication, anyone can send fake events to your BigQuery dataset. You need API key validation, request signing, IP allowlisting, or service account authentication. That’s not a weekend—that’s a security architecture decision.

Error Handling and Retry Logic

BigQuery’s Streaming Insert API has a 99.9% SLA. That remaining 0.1% means events will fail. Without retry logic with exponential backoff, you lose data. Without a dead letter queue for permanently failed events, you can’t investigate or replay failures. A pipeline without retry logic is a pipeline that silently loses your conversion data.

Schema Evolution

WooCommerce updates. Your theme changes. You add custom checkout fields. Every schema change requires updating your Cloud Run function, your BigQuery table schema, and potentially backfilling historical data. In a productized solution, this is handled automatically. In DIY, this is Tuesday at 2 AM when your pipeline breaks.

Monitoring and Alerting

How do you know when your pipeline stops working? If Cloud Run returns errors, who gets notified? If BigQuery rejects rows due to schema mismatch, where does that alert go? Without monitoring, your pipeline can silently fail for days—and you discover it when your weekly BigQuery report shows a suspicious gap.

You may be interested in: WooCommerce Webhooks Are Not 100% Reliable: Why Server-Side Event Processing Beats Webhook Architecture

The Real Time Investment#

Let’s be honest about the timeline:

Weekend 1-2: Prototype. Cloud Run endpoint, basic BigQuery insert, WordPress hook integration. It works on your dev site. You feel great.

Week 3-8: Production hardening. Authentication, retry logic, dead letter queues, monitoring, staging environment, load testing. You’re writing infrastructure code, not business logic.

Month 3 onwards: Maintenance forever. Custom pipeline maintenance averages 4-8 hours per month for schema updates, dependency patches, error investigation, and scaling adjustments (DevOps engineering estimates, 2025). At $100/hour for developer time, that’s $4,800-$9,600/year in maintenance alone—before counting the initial build.

And that’s for a single destination. You’ve built a WordPress-to-BigQuery pipeline. What about Facebook CAPI? Google Ads Enhanced Conversions? Klaviyo? Each additional destination means another API integration, another set of data formatting requirements, another authentication flow, another monitoring setup.

The Honest Cost Comparison#

Here’s what both paths actually cost over two years:

DIY Path:

• Infrastructure: $5-$20/month (Cloud Run, Pub/Sub, BigQuery)—roughly $240-$480 over two years
• Initial build to production: 80-160 hours at $100/hour = $8,000-$16,000
• Ongoing maintenance: 4-8 hours/month × 24 months × $100/hour = $9,600-$19,200
• Total two-year cost: $17,840-$35,680 for a single BigQuery destination

Productized Path:

• Subscription: $89-$259/month × 24 months = $2,136-$6,216
• Setup time: Under one hour
• Maintenance: Zero engineering hours
• Destinations included: BigQuery + GA4 + Facebook CAPI + Google Ads + more
• Total two-year cost: $2,136-$6,216 for multiple destinations

The infrastructure cost is a rounding error. The engineering time is where DIY gets expensive.

When DIY Makes Sense#

This isn’t an anti-DIY argument. Building your own pipeline is the right call in specific situations:

You’re a developer who wants to learn Google Cloud. The pipeline project teaches Cloud Run, Pub/Sub, BigQuery, IAM, and monitoring. That’s valuable career education worth the time investment.

You need a single, custom destination with a unique schema. If your data warehouse requires a proprietary format that no productized solution supports, custom is your only option.

You enjoy maintaining infrastructure. Some developers genuinely find pipeline maintenance rewarding. If debugging schema mismatches at midnight energizes you, build away.

Your WooCommerce store IS your side project. If the store itself is a learning exercise, adding infrastructure complexity is part of the fun.

When DIY Doesn’t Make Sense#

For everyone else—and that’s most of the 43.5% of websites running WordPress (W3Techs, 2024)—the math doesn’t work:

You’re a store owner, not an infrastructure engineer. Your time is better spent on products, marketing, and customers than on pipeline maintenance.

You need multiple destinations. BigQuery alone is rarely enough. You need GA4 for real-time reporting, Facebook CAPI for ad optimization, Google Ads Enhanced Conversions for bidding. Each destination multiplies the DIY maintenance burden.

You can’t afford silent failures. When your DIY pipeline breaks on Black Friday, there’s no support team to call. Every missed conversion event is lost revenue data you can’t recover.

Transmute Engine™ is what the DIY approach becomes after two years of production hardening—a first-party Node.js server running on your subdomain that handles event capture, formatting, retry logic, and multi-destination routing. The difference is you don’t pay the engineering hours to get there.

Key Takeaways#

• The DIY prototype works. Cloud Run + Pub/Sub + BigQuery is a sound architecture, and infrastructure costs stay under $20/month for most stores.
• Production readiness adds 5-10x the effort. Authentication, retry logic, monitoring, and error handling transform a weekend project into a months-long engineering effort.
• Ongoing maintenance costs $4,800-$9,600/year at typical developer rates—for a single destination.
• Multi-destination multiplies complexity. Each additional platform (Facebook CAPI, GA4, Google Ads) means another API integration to build and maintain.
• Build if you’re learning. Buy if you’re running a business. The right choice depends on whether you’re investing in skills or investing in outcomes.

Ready to skip the maintenance and go straight to production-grade tracking? See how Transmute Engine works →