When selecting a destination, it’s important to first verify that all the data sources you want to connect to Stitch will be compatible.
We recommend verifying your integrations’ compatibility before connecting a destination. This will ensure that you can successfully connect and replicate data from all your sources.
Degrees of incompatibility
The compatibility of any integration/destination combination falls into one of three categories: always compatible, sometimes compatible, and never compatible.
The matrices below use the following icons to indicate the degree of incompatibility for an integration/destination combo:
- indicates that this combo is sometimes compatible - there may be compatibility issues, but they’re infrequent or parts of the integration may still be usable.
- indicates that this combo is never compatible. It’s unlikely that Stitch will be able to load data from this integration into the given destination.
Incompatible integrations by destination type
Below you’ll find a list of integrations that may have full or partial incompatibility with any of Stitch’s destination offerings.
Refer to the Destination data loading guides for a comprehensive look at how destinations will load data, including what may cause data to be rejected.
| Amazon S3 (link) | |||
| No compatibility issues have been discovered between Amazon S3 and Stitch's integration offerings. | |||
| Google BigQuery v1 (link) | |||
| No compatibility issues have been discovered between this version (v1) of the Google BigQuery destination and Stitch's integration offerings. | |||
| Google BigQuery v2 (link) | |||
| No compatibility issues have been discovered between this version (v2) of the Google BigQuery destination and Stitch's integration offerings. | |||
| data.world (link) | |||
| No compatibility issues have been discovered between data.world and Stitch's integration offerings. | |||
| Databricks Delta Lake (AWS) (link) | |||
| No compatibility issues have been discovered between Databricks Delta Lake (AWS) and Stitch's integration offerings. | |||
| Microsoft Azure Synapse Analytics (link) | |||
| No compatibility issues have been discovered between Microsoft Azure Synapse Analytics and Stitch's integration offerings. | |||
| Microsoft SQL Server (link) | |||
| No compatibility issues have been discovered between Microsoft SQL Server and Stitch's integration offerings. | |||
| MySQL (link) | |||
| No compatibility issues have been discovered between MySQL and Stitch's integration offerings. | |||
| Panoply (link) | |||
| Integration | Version | Level | Reason |
| MongoDB | ANY |
Flattening nested JSON structures may result in tables with columns that exceed Panoply’s 1,600 column limit. Learn more. |
|
| MongoDB Atlas | v1 |
Flattening nested JSON structures may result in tables with columns that exceed Panoply’s 1,600 column limit. Learn more. |
|
| PostgreSQL (link) | |||
| Integration | Version | Level | Reason |
| HubSpot | ANY |
Flattening nested JSON structures may result in tables and columns with names that exceed PostgreSQL’s character limit for object names:
|
|
| Stripe | ANY |
Flattening nested JSON structures may result in tables and columns with names that exceed PostgreSQL’s character limit for object names:
|
|
| Amazon Redshift (link) | |||
| Integration | Version | Level | Reason |
| MongoDB | ANY |
Flattening nested JSON structures may result in tables with columns that exceed Amazon Redshift’s 1,600 column limit. Learn more. |
|
| MongoDB Atlas | v1 |
Flattening nested JSON structures may result in tables with columns that exceed Amazon Redshift’s 1,600 column limit. Learn more. |
|
| Snowflake (link) | |||
| No compatibility issues have been discovered between Snowflake and Stitch's integration offerings. | |||
| Related | Troubleshooting |
Questions? Feedback?
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