The Data Warehouse Decision: BigQuery, Supabase, or Both?

"Where should we store our data?" is one of the first questions every growing business faces. It's also one of the most consequential — the wrong choice doesn't just cost money, it shapes (and limits) everything you can build on top.

We've run both BigQuery and Supabase (PostgreSQL) in production across multiple client engagements. Here's what we've learned about when to use each — and why the real answer is usually both.

BigQuery: The Analytical Powerhouse

BigQuery is Google's serverless data warehouse. It's designed for one thing: running fast queries over massive datasets.

Use BigQuery when you need to:

• Analyze months or years of historical data
• Run complex aggregations across millions of rows
• Power BI dashboards and reporting
• Store data cheaply that you query infrequently
• Process batch ETL jobs on a schedule

BigQuery strengths:

• Storage is incredibly cheap (~$0.02/GB/month)
• Can scan terabytes in seconds
• SQL interface — no new query language to learn
• Scales automatically with no capacity planning
• Excellent integration with Google Cloud services

BigQuery limitations:

• Not designed for real-time reads/writes
• No row-level security out of the box
• Minimum query latency is ~500ms (not suitable for app backends)
• Streaming inserts cost more than batch loads

Supabase: The Application Database

Supabase wraps PostgreSQL with real-time subscriptions, authentication, a REST API, and a dashboard. It's designed to be the backend for applications.

Use Supabase when you need to:

• Serve data to a web or mobile app in real-time
• Handle user authentication and row-level security
• Build CRUD operations with a REST or GraphQL API
• React to data changes with real-time subscriptions
• Store operational data that changes frequently

Supabase strengths:

• Sub-millisecond query latency for indexed reads
• Built-in auth, RLS, and API generation
• Real-time subscriptions for live dashboards
• Full PostgreSQL power (triggers, functions, extensions)
• Generous free tier for getting started

Supabase limitations:

• Not ideal for multi-terabyte analytical queries
• Requires capacity planning (compute scales, but not infinitely)
• Complex analytical queries can be slow without careful indexing

The Real Answer: Use Both

In our production system, BigQuery and Supabase serve fundamentally different roles:

• BigQuery is the data warehouse — the historical record. All raw data from POS, scheduling, inventory, and accounting lands here first. Nightly batch jobs transform it into analytical views. This is where we answer "how did last quarter compare to this quarter?"
• Supabase is the operational database — the live system. Processed, current data syncs from BigQuery to Supabase on a schedule. This powers our dashboards, our app, and our real-time alerts. This is where we answer "what do we need to prep for tomorrow?"

The data flows in one direction:

Source Systems → BigQuery (warehouse) → Supabase (operational)
   (POS, etc.)      (transform, store)     (serve to apps)

Decision Framework

If you're just starting out and need to pick one:

• Building an app? Start with Supabase. You'll need the real-time API and auth.
• Building analytics? Start with BigQuery. You'll need the query performance and cheap storage.
• Building both? Start with wherever the most urgent need is, then add the other when you hit the limits.

Don't try to make one tool do everything. BigQuery as an app backend is painful. Supabase as a data warehouse gets expensive fast. Use each for what it's designed for.

The Sync Challenge

The hardest part of running both isn't choosing — it's keeping them in sync. Your BigQuery transformations need to produce data in a format Supabase can consume. Schema changes need to propagate. Stale data needs to be detected and refreshed.

This is where a well-designed data pipeline earns its keep. Invest in monitoring and validation at the boundary between your warehouse and your operational database. That's where things break.