I need a warehouse that can replace ad hoc SQL on production databases

Yes — that’s a common reason to introduce a warehouse.

A good target is a separate analytics warehouse that is kept in sync from production databases, so analysts and engineers stop querying prod directly. The usual pattern is:

Production DBs → CDC / ETL → Warehouse → BI / ad hoc SQL

What to look for

A warehouse for this use case should have:

• Fast read performance for large scans and joins
• Isolation from production so heavy queries don’t affect app latency
• Easy SQL access for ad hoc analysis
• Support for incremental ingestion from OLTP databases
• Governance/security: permissions, masking, auditing
• Reasonable cost for unpredictable query loads

Common choices

• Snowflake — strong for ad hoc analytics, easy to use, good concurrency
• BigQuery — great if you’re in GCP / want serverless
• Redshift — good if you’re on AWS and want tighter ecosystem integration
• Databricks SQL / Lakehouse — useful if you already have Spark/Delta workflows
• ClickHouse — very fast for analytics, often excellent for high-volume querying
• Postgres read replica — only if the scale is modest; not a true warehouse replacement

Typical implementation

1. Replicate data from production using:
   • CDC tools like Debezium, Fivetran, Airbyte, AWS DMS
   • Or batch ELT if near-real-time isn’t needed
2. Model data into analytics-friendly tables:
   • denormalized facts/dimensions
   • curated views for common queries
3. Point ad hoc users to the warehouse instead of prod
4. Restrict prod access to break-glass / operational needs only

If your main goal is “replace random SQL on prod”

Prioritize:

• low-latency ingestion
• simple SQL compatibility
• workload isolation
• strong BI/ad hoc concurrency

If you tell me:

• your cloud provider,
• database(s),
• data size,
• freshness requirements,
• and budget sensitivity,

I can recommend a specific warehouse and ingestion architecture.