--- title: "Data warehouse sync: Snowflake, BigQuery, Databricks" description: "Connect warehouse data with operational applications. Compare ingestion, reverse ETL, and two-way sync, then verify supported tables and write directions." canonical: https://www.stacksync.com/data-warehouse last_modified: 2026-09-15 --- DATA WAREHOUSE # Connect warehouse data to operational work. Bring operational data into your warehouse and send actionable data back to business applications. Manage mappings, sync direction, and monitoring from one platform. [Book a demo](https://www.stacksync.com/book-a-demo) WAREHOUSE CONNECTORS ## Explore warehouse connection options. Select your warehouse and review its supported tables and access requirements. Plan ingestion and return writes separately, with an explicit owner for each derived field. Cloud warehouse ### [Snowflake](https://www.stacksync.com/connectors/snowflake) Connect supported Snowflake tables. Check primary keys, service-role grants, and excluded object types. AWS · GCP · Azure See connector Cloud warehouse ### [BigQuery](https://www.stacksync.com/connectors/bigquery) Review BigQuery table support, service-account permissions, and notification setup before enabling writes. GCP See connector Lakehouse ### [Databricks](https://www.stacksync.com/connectors/databricks) Define the Databricks tables and operational destination, then verify the selected connector requirements. Delta · Unity See connector Serverless DuckDB ### [MotherDuck](https://www.stacksync.com/connectors/motherduck) Review the MotherDuck connection requirements for the data and permitted directions in your workflow. DuckDB · Cloud See connector Real-time OLAP ### [ClickHouse](https://www.stacksync.com/connectors/clickhouse) Check the ClickHouse connector scope and the required ingestion or operational write-back path. Self-host · Cloud See connector ODS · Replica ### [Postgres](https://www.stacksync.com/connectors/postgresql) Use supported Postgres tables for selected operational data. Verify keys, replication rights, and write permissions. RDS · Aurora · Self See connector Which warehouse data flow fits the workflow? Warehouse integration brings source data into analytics and can publish prepared values to operational applications. Choose ingestion, reverse ETL, or two-way sync according to field ownership. Stacksync’s Snowflake connector supports standard tables; ordinary and materialized views are marked Coming soon, and external tables are excluded. [Review Snowflake table support.](https://docs.stacksync.com/two-way-sync/connectors/snowflake) ## Warehouse integration guides and resources Document which fields the warehouse owns and which remain editable in the application. Include model-refresh time and destination API capacity in the freshness target. - [deep dive Acumatica–Postgres Sync for Beverage Operations: Match Case and Pallet Records Across Warehouses](https://www.stacksync.com/blog/acumatica-postgres-sync-for-beverage-operations-match-case-and-pallet-records-across-warehouses) - [comparison ETL vs Reverse ETL vs Two-Way Sync: Choose the Data Flow](https://www.stacksync.com/blog/etl-vs-reverse-etl-guide-modern-data-integration) - [article GitHub Into Snowflake Is Solved. The Trip Back Is Not.](https://www.stacksync.com/blog/two-way-sync-github-snowflake) - [article What an Integration Platform Has to Get Right on MotherDuck](https://www.stacksync.com/blog/enterprise-ipaas-motherduck) FAQ ## Frequently asked questions ### How do ingestion, reverse ETL, and two-way sync differ? Ingestion brings source data into the warehouse. Reverse ETL publishes prepared attributes to operational applications. Two-way sync coordinates selected edits in both directions. Choose by who owns the fields and which system may change them; timing is a separate design decision. [ETL, reverse ETL, and two-way sync guide](https://www.stacksync.com/blog/etl-vs-reverse-etl-guide-modern-data-integration) ### Can I use any Snowflake query or view as a sync source? No. The Snowflake connector documents standard tables, marks ordinary and materialized views as Coming soon, and excludes external tables. Existing tables also need the documented primary key and default. Validate a supported table-based design for the intended data flow. [Snowflake supported objects](https://docs.stacksync.com/two-way-sync/connectors/snowflake) ### Does the BigQuery connector support views? The BigQuery guide supports tables and excludes ordinary and materialized views. Authorization requires a dedicated Google Cloud service account, the documented APIs and roles, and notification setup using Eventarc and Cloud Run. Confirm project quotas before a large load. [BigQuery connector requirements](https://docs.stacksync.com/two-way-sync/connectors/bigquery) ### How should we measure warehouse-to-CRM freshness? Measure from the source business change through model preparation, change detection, queued work, and the accepted CRM write. Include the warehouse refresh schedule and destination API budget. A fast final write cannot make an older warehouse result current. [API budget configuration](https://docs.stacksync.com/two-way-sync/features/manage-api-rate-limits) ### Should warehouse-derived scores be writable in both systems? Usually the design gives a derived score one owner and lets the CRM own separate operational fields. If both systems edit the same value, define and test the supported conflict behavior. Read-only or computed fields do not become writable simply because the connection supports two-way sync. [Direction and writable-field requirements](https://docs.stacksync.com/two-way-sync/features/two-way-sync) GET STARTED ## Syncing warehouse data at scale across every industry. - POC from integration engineers - Two-way, real-time sync - CDC + reverse ETL - White-glove onboarding [Book a demo](https://www.stacksync.com/book-a-demo) FROM A CUSTOMER “We've been using Stacksync across 4 different projects and can't imagine working without it.” Alex Marinov VP Technology, Acertus Delivers Vehicle logistics powered by technology