Difference Between BigQuery and Snowflake Warehouse Connectors

Based on the provided knowledge sources, there are some key differences in how Mixpanel's Warehouse Connector works with BigQuery and Snowflake: 1. Change tracking mechanism: - For BigQuery, Mirror (a sync mode) uses [table snapshots](https://cloud.google.com/bigquery/docs/table-snapshots-intro) to track changes between sync runs [Docs > Tracking-methods > Warehouse Connectors: Sync data from your data warehouse into Mixpanel > FAQ > What will be the cost impact of this on my DWH?](https://docs.mixpanel.com/docs/tracking-methods/data-warehouse#what-will-be-the-cost-impact-of-this-on-my-dwh). - For Snowflake, Mirror uses [Snowflake Streams](https://docs.snowflake.com/en/user-guide/streams-intro) to track changes to tables or views [Docs > Tracking-methods > Warehouse Connectors: Sync data from your data warehouse into Mixpanel > Sync Modes > Mirror Part 3](https://docs.mixpanel.com/docs/tracking-methods/data-warehouse#mirror). 2. Setup requirements: - BigQuery requires granting specific permissions to a Mixpanel-managed service account and creating a new, empty mixpanel dataset [Docs > Tracking-methods > Warehouse Connectors: Sync data from your data warehouse into Mixpanel > Getting Started](https://docs.mixpanel.com/docs/tracking-methods/data-warehouse#getting-started). - Snowflake requires enabling change tracking on the table or view to be synced [Docs > Tracking-methods > Warehouse Connectors: Sync data from your data warehouse into Mixpanel > Sync Modes > Mirror Part 3](https://docs.mixpanel.com/docs/tracking-methods/data-warehouse#mirror). 3. Partitioned tables: - BigQuery can use partition metadata to skip processing unchanged partitions, making it more efficient for large partitioned tables [Docs > Tracking-methods > Warehouse Connectors: Sync data from your data warehouse into Mixpanel > Sync Modes > Mirror Part 3](https://docs.mixpanel.com/docs/tracking-methods/data-warehouse#mirror). - There's no specific mention of partitioned table handling for Snowflake in the provided knowledge sources. 4. Compute costs: - For BigQuery, the connector checksums all rows in the source table and compares them to a table snapshot from the previous run, which can be computationally expensive for large tables [Docs > Tracking-methods > Warehouse Connectors: Sync data from your data warehouse into Mixpanel > FAQ > What will be the cost impact of this on my DWH?](https://docs.mixpanel.com/docs/tracking-methods/data-warehouse#what-will-be-the-cost-impact-of-this-on-my-dwh). - For Snowflake, the compute cost is typically proportional to the amount of changed data, as Snowflake Streams natively track changes [Docs > Tracking-methods > Warehouse Connectors: Sync data from your data warehouse into Mixpanel > FAQ > What will be the cost impact of this on my DWH?](https://docs.mixpanel.com/docs/tracking-methods/data-warehouse#what-will-be-the-cost-impact-of-this-on-my-dwh).