Can you help me choose between cloud warehouses for a growing analytics team?

Absolutely — I can help you compare cloud data warehouses and pick the best fit for a growing analytics team.

The short version

For most teams today, the main choices are:

• Snowflake — best all-around choice for ease of use, sharing, and multi-cloud flexibility
• BigQuery — best if you’re already deep in Google Cloud / want minimal ops and very fast scaling
• Redshift — best if you’re strongly committed to AWS and want tight AWS integration
• Databricks SQL / Lakehouse — best if your “warehouse” needs overlap heavily with ML, data engineering, and open data lake architecture

How to choose

Here are the most important decision factors:

1) Team skill and operational overhead

• Lowest operational burden: BigQuery, Snowflake
• More tuning/management: Redshift
• More platform complexity but broader use cases: Databricks

If your team wants to focus on analysis instead of infrastructure, Snowflake or BigQuery usually win.

2) Cloud ecosystem

• Already on AWS: Redshift or Snowflake
• Already on GCP: BigQuery
• Already on Azure: Snowflake or Fabric if Microsoft-native tools matter

3) Cost model

• BigQuery: great for bursty workloads; can get expensive with poorly optimized queries
• Snowflake: predictable-ish, but compute/storage separation means you must manage warehouses well
• Redshift: can be cost-effective for steady workloads, but tuning matters
• Databricks: strong if you’re consolidating multiple workloads, but not always cheapest for pure BI

4) Analytics workflow

• BI-heavy, ad hoc SQL, sharing: Snowflake or BigQuery
• Complex data engineering + ML + SQL: Databricks
• Traditional warehouse with AWS-native tooling: Redshift

5) Data sharing and collaboration

• Best built-in sharing: Snowflake
• Good collaboration in GCP-native environments: BigQuery
• Less seamless external sharing: Redshift

A practical recommendation

If you’re a growing analytics team and don’t have a strong cloud lock-in yet:

• Pick Snowflake if you want the safest general-purpose option
• Pick BigQuery if you’re already on GCP or expect highly elastic, ad hoc usage
• Pick Redshift if your company is AWS-first and cost control via infrastructure is important
• Pick Databricks if analytics is tightly coupled with ML/data engineering and you want a lakehouse approach

Quick rule of thumb

• Need the simplest analyst experience? Snowflake
• Need fastest setup on GCP? BigQuery
• Need AWS-native and possibly cheaper at scale with tuning? Redshift
• Need SQL + ML + pipelines in one platform? Databricks

If you want, I can do a side-by-side comparison table or help you choose based on your exact situation, like:

• cloud provider
• team size
• BI tools
• data volume
• budget
• engineering maturity