Stop waiting on batch loads.

Stacksync syncs natively with Snowflake, Google BigQuery, Databricks, Amazon Redshift, MotherDuck, ClickHouse, and Postgres-on-warehouse-mode setups like Neon and Supabase. Each connector handles bulk loads, incremental CDC, and reverse-ETL writes back into the warehouse. New warehouses are added on request — most go from request to production support within 3 weeks.

Yes — warehouse-to-app and app-to-warehouse both work in the same Stacksync connection. The most common pattern is computing lead scores or account health metrics in Snowflake and pushing them into Salesforce or HubSpot every few seconds, while simultaneously syncing raw CRM events into the warehouse for analytics. Bidirectional sync includes conflict resolution and per-field direction policy.

Fivetran and Airbyte are unidirectional EL tools optimized for periodic batch loads — they move data from sources into a warehouse on a schedule (every 5 minutes at best). Stacksync is bidirectional and streaming: warehouse-to-app writes are a first-class feature (reverse-ETL is built-in, not bolted on), and the streaming path achieves sub-second latency for sources that expose change events. Per-record pricing is also more predictable than Fivetran for high-volume CDC workloads.

Reverse ETL is the core use case. Define a Snowflake query that produces account scores, customer LTV, churn risk, or any computed metric, and Stacksync pushes the result into Salesforce, HubSpot, Marketo, or any of 200+ destinations on the same schedule the query refreshes. Updates only push changed rows (Stacksync diffs against the previous run) so destination API budgets are not consumed by no-op writes.

For streaming sources (warehouse query refreshed on a continuous Snowflake task or BigQuery scheduled query), Stacksync delivers row-level updates to the destination app within 5 to 30 seconds. For traditional batch queries refreshed on an hourly or daily cadence, Stacksync triggers a sync immediately after the warehouse job completes. End-to-end latency from warehouse update to app field change is typically under 60 seconds even on hourly schedules.

Stacksync detects schema changes on every sync run and adapts mappings automatically when changes are additive (new columns, renamed columns with type compatibility). Breaking changes (column drops, incompatible type changes) are surfaced in the dashboard as a warning before sync degrades. For warehouse-to-app reverse ETL, schema changes in the source query are propagated to the destination only after explicit approval to prevent accidental field overwrites.

Yes — warehouse-to-warehouse is supported including Snowflake to BigQuery, Snowflake to Databricks, BigQuery to Redshift, and Postgres to any warehouse. The pattern is common for multi-cloud strategies where teams need analytics in one warehouse and operational data in another. Stacksync uses bulk-export and bulk-load APIs at each end so the data path stays cheap and fast even at billions of rows.