BigQuery (GCP), Snowflake, and Amazon Redshift are the three dominant cloud data warehouses (DWHs) today. This article compares them across architecture, pricing, performance, ML integration, multi-cloud support, Data Sharing, and learning curve, then offers concrete recommendations by use case.
| Aspect | BigQuery | Snowflake | Redshift |
|---|---|---|---|
| Cloud | GCP only (AWS/Azure via Omni) | AWS / Azure / GCP | AWS only |
| Architecture | Fully serverless | Decoupled Virtual Warehouse (compute) | Cluster / Serverless |
| Pricing unit | Bytes scanned / Slot | Warehouse hours | Cluster hours / Serverless RPU |
| Free tier | 1 TB queries + 10 GB storage / month | $400 credit (30 days) | $300 credit (90 days) |
| SQL dialect | GoogleSQL | Snowflake SQL | PostgreSQL-compatible |
| Item | BigQuery on-demand | Snowflake (Standard) | Redshift Serverless |
|---|---|---|---|
| Query | $6.25 (1 TB × $6.25) | ~$10-30 (depends on warehouse hours) | ~$30 (RPU hours) |
| Storage | $2 (100 GB × $0.02) | $2.30 (100 GB × $23/TB) | $2.4 (100 GB) |
| Estimated total | ~$8 | ~$15-35 | ~$35 |
| Aspect | BigQuery | Snowflake | Redshift |
|---|---|---|---|
| Query parallelism | Thousands of slots, automatic | Warehouse size | Number of nodes |
| Concurrency | Unlimited (within slot capacity) | Multi-cluster Warehouse | 50 concurrent (Serverless) |
| BI acceleration | BI Engine ($30/GB/month) | Cortex Search | Materialized View |
| Scale Up/Down | Automatic | Switch warehouse | Resize / Concurrency Scaling |
| Aspect | BigQuery | Snowflake | Redshift |
|---|---|---|---|
| SQL ML | BigQuery ML (10+ algorithms) | Cortex (LLM + ML) | Redshift ML |
| LLM integration | Gemini (ML.GENERATE_TEXT) | Cortex LLM (Claude / Mistral) | Amazon Q + Bedrock |
| Python | BigQuery DataFrames | Snowpark | SageMaker integration |
| Vector Search | VECTOR_SEARCH | Cortex Search | pgvector (Aurora) |
| Aspect | BigQuery | Snowflake | Redshift |
|---|---|---|---|
| Multi-cloud | BigQuery Omni (query AWS/Azure) | Native (excellent) | — |
| Data Sharing | Analytics Hub | Secure Data Sharing (most mature) | Data Sharing |
| Marketplace | Analytics Hub | Snowflake Marketplace (largest) | AWS Data Exchange |
| Iceberg | BigLake | Iceberg Tables (native) | Spectrum + Iceberg |
| Use case | Recommendation | Why |
|---|---|---|
| Bursty queries + cost-optimized | BigQuery | On-demand pricing is the cheapest |
| GA4 / Google Ads analytics | BigQuery | Native integration |
| Multi-cloud is a hard requirement | Snowflake | Spans AWS/Azure/GCP |
| Data Sharing / Marketplace | Snowflake | Largest marketplace |
| AWS ecosystem integration | Redshift | Strong S3 / Glue / Lambda affinity |
| GenAI-integrated analytics | BigQuery + Gemini | Call LLMs directly from SQL |
| Python ML pipelines | Snowflake + Snowpark / Databricks | OSS compatibility + container support |
Which is cheapest: BigQuery, Snowflake, or Redshift?
It depends on the workload. BigQuery on-demand is ideal for bursty queries, Snowflake favors efficiency with reserved compute, and Redshift Serverless offers stability with deep AWS integration. For 1 TB of queries per month, BigQuery is the cheapest.
Does Snowflake support multi-cloud?
Yes. You can replicate the same data across AWS, Azure, and GCP and query it from any of them. If multi-cloud or Data Sharing is a hard requirement, Snowflake has the edge.
What are BigQuery's unique strengths?
Fully serverless architecture, Gemini integration (ML.GENERATE_TEXT), BigQuery ML, generous free tier (1 TB queries / month), native GA4 / Google Ads integration, and BigQuery Omni for multi-cloud queries.
What are Redshift's unique strengths?
Deep AWS ecosystem integration (S3, Glue, Lambda), direct S3 querying via Spectrum, Aurora ML integration, and Bring Your Own Model support.
What are Snowflake's unique strengths?
Multi-cloud (AWS/Azure/GCP), Data Cloud and Marketplace, Snowpark (Python/Java/Scala), native Iceberg support, and Time Travel / Zero Copy Clone.
Should I pick one of these or Databricks Lakehouse?
If your workload is DWH-centric, pick one of the three. For ML pipelines or OSS-friendly stacks, Databricks is a strong choice. That said, BigQuery + Vertex AI has been closing the gap against Databricks.
How easy is Data Sharing on each platform?
Snowflake's Secure Data Sharing is the most mature. BigQuery offers Analytics Hub and Redshift has Data Sharing. Snowflake leads, but BigQuery is catching up fast.
What is the learning curve like?
BigQuery has the gentlest learning curve thanks to standard SQL and a simple GUI. Snowflake has its own concepts (Warehouse / Database / Schema hierarchy), and Redshift assumes existing AWS knowledge.
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* All product names are trademarks of their respective owners. Please check each vendor's official site for the latest pricing.
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