We Benchmark-Tested 5 Data Warehouses. Here's What Broke.
Choosing a data warehouse shouldn’t feel like a gamble — but it often is.
Marketing sites are polished. Demos are cherry-picked. Docs are full of high-level promises. But when your data team starts moving terabytes of real data, things change fast: performance bottlenecks, cost spikes, memory errors… and sometimes complete failure.
At Estuary, we help teams build real-time data pipelines that push warehouses hard — across batch and streaming. We’ve seen the consequences of choosing the wrong warehouse. So we built the benchmark we wish existed earlier.
🔍 The Estuary 2025 Data Warehouse Benchmark
We benchmarked 5 major data warehouses under real workloads:
- Google BigQuery
- Snowflake
- Databricks
- Amazon Redshift
- Microsoft Fabric
We didn’t just run canned TPCH queries — we loaded over 8TB of structured + semi-structured data, then hit each platform with real-world SQL:
- Joins, window functions, filters, and nesting
- Query-F (“The Frankenquery”) — a deliberately brutal query that pushes limits
- Full lifecycle tracking from ingest to query via Estuary Flow
- Cost-to-runtime ratios with no vendor tuning or caching games
📂 Our full methodology is open source. Clone it. Run your own tests. Contribute.
🧠 What We Learned
🔵 BigQuery
- Fast — especially on nested JSON
- But zero cost guardrails = high bill risk
- Cost-per-minute hit $15+ under some setups
⚪ Snowflake
- Stable, predictable, smart scaling
- Good balance of performance and cost
- Strong default choice for teams who want reliability
🟨 Databricks
- Great for ML workflows
- SQL under load? Needs tuning
- Performance quirks at scale
🟥 Redshift & 🟩 Fabric
- Memory errors, long runtimes, incomplete results
- Multiple queries failed or stalled for hours
- Definitely not plug-and-play ready
📉 Chart: Cost vs Runtime
This graph tracks $ per minute of query runtime across warehouses and instance sizes.
Red bands = platforms that failed under load or threw memory errors.
⚙️ Rankings That Actually Matter
We scored each platform on:
- Cost-efficiency 💰
- Runtime performance ⚡
- Scalability 📈
- Reliability under pressure 🧱
- Startup-friendliness 🚀
- Enterprise readiness 🏢
🎯 Some platforms were efficient at small scale but crashed under growth. Others performed well but cost 10x more than peers.
📥 Get the Full Report
If you’re:
- Planning a warehouse migration
- Scaling analytics or ML pipelines
- Comparing Snowflake vs BigQuery vs Databricks
- Or just tired of guessing…
👉 Download the full benchmark report
👨🔬 Built by Engineers, Not Marketers
We created this benchmark at Estuary because we work with these warehouses daily. Our product — Estuary Flow — streams real-time data from sources like PostgreSQL, Kafka, MongoDB, and SaaS apps into modern warehouses.
We’ve helped teams recover from 18-month migrations and $100k+ in wasted compute. So we’re publishing what we’ve learned.
🤝 Contribute or fork the test harness here:
🔗 GitHub Repo
🌐 Estuary GitHub
💬 Join the Discussion
Have you had similar (or better?) experiences with these platforms?
Spot something we should test next?
Drop your thoughts, logs, or horror stories in the comments. We’re all ears 👇