Snowflake

Snowflake is the data warehouse we recommend when BigQuery isn't the right fit — and that's not a knock on Snowflake. It's a genuinely excellent product that does some things better than BigQuery. The separated compute/storage model, multi-cloud support, and data sharing capabilities are real differentiators. For clients with specific performance requirements or AWS/Azure-first infrastructure, Snowflake is often the better choice.

Snowflake is the data warehouse we recommend when BigQuery isn't the right fit — and that's not a knock on Snowflake. It's a genuinely excellent product that does some things better than BigQuery. The separated compute/storage model, multi-cloud support, and data sharing capabilities are real differentiators. For clients with specific performance requirements or AWS/Azure-first infrastructure, Snowflake is often the better choice.

Where It Excels

Snowflake's architecture lets you scale compute independently of storage, which means you can spin up dedicated warehouses for different workloads without contention. Need a small warehouse for ad-hoc queries and a large one for nightly transforms? Easy. This matters for clients who've grown past the point where a single shared compute layer works. Query performance on well-structured data is also noticeably faster than BigQuery for complex joins and aggregations.

The data sharing features are increasingly relevant. If a client needs to share clean datasets with partners or vendors without moving data around, Snowflake handles this natively. The multi-cloud support (AWS, Azure, GCP) also gives clients flexibility that BigQuery can't match.

Where It Falls Short

Snowflake's credit-based pricing is harder to predict than BigQuery's query-based model. Credits scale with warehouse size and runtime — a Medium warehouse at 4 credits/hour running 8 hours/day at $3/credit (Enterprise) is nearly $3,000/month just for one warehouse. For growth-stage companies, this can escalate quickly if warehouses aren't configured to auto-suspend properly. We've seen clients burn through credits with always-on warehouses that should've been suspended.

There's no meaningful free tier for production use. The 30-day trial is generous, but ongoing costs start from day one. For clients who are just getting started with data centralization and have modest query volumes, BigQuery's free tier and pay-per-query model is more forgiving.

How JMK Uses It

Snowflake is our alternative data warehouse recommendation. We deploy it for clients who are already on AWS, need the performance of dedicated compute, or have data sharing requirements. Standard setup mirrors our BigQuery pattern: n8n pipelines load raw data, dbt handles transformations, and Metabase or Looker connects for visualization. We always configure auto-suspend (5-minute timeout) and resource monitors to prevent cost overruns.

Who It's Right For

• Companies with AWS or Azure as their primary cloud provider where staying in-ecosystem matters.
• Businesses with heavy analytical workloads that benefit from dedicated, scalable compute.
• Organizations that need to share data natively with external partners or vendors.
• Teams that have outgrown BigQuery's on-demand pricing and need more predictable compute costs.

Who Should Look Elsewhere

• Early-stage companies with modest data needs — BigQuery's free tier is a better starting point.
• Teams without someone comfortable managing warehouse sizing and cost controls.
• Companies on tight budgets who need the simplest possible pricing model.