This table outlines the compatibility of various datasources with quality. Understanding this compatibility is important to optimize data quality, ensuring that relevant metrics and logs can be effectively monitored and analyzed.
| Datasource | Data Access | ||||||
|---|---|---|---|---|---|---|---|
| Deployment Method | Pipeline Engine | Sample Generation | Run Default Rules | Run Custom Rules | Run Data Quality Pipelines | Triggered cataloging upon Data Quality pipeline execution | |
| Databricks | Cloud | Spark in Databricks | Yes | Yes | Yes | Yes | Yes |
| Databricks | On-premise | Spark in Databricks | Yes | Yes | Yes | Yes | Yes |
| Snowflake | Cloud | Snowpark | Yes | Yes | Yes | Yes | Yes |
| Snowflake | On-premise | Snowpark | Yes | Yes | Yes | Yes | Yes |
| Microsoft SQL Server | On-premise | Precisely Agent | Yes | Yes | Yes | Yes | Yes |
| Oracle | On-premise | Precisely Agent | Yes | Yes | Yes | Yes | Yes |
| Azure SQL Server | On-premise | Precisely Agent | Yes | Yes | Yes | Yes | Yes |
| Microsoft Azure Synapse Analytics | On-premise | Precisely Agent | Yes | Yes | Yes | Yes | Yes |
| PostgreSQL | On-premise | Precisely Agent | Yes | Yes | Yes | Yes | Yes |
To configure each datasource, refer to the Supported datasources. This
documentation directs you to step-by-step instructions, including necessary parameters,
and connection settings for each datasource.
Note:
- Data quality now supports cross-connection (Oracle & SQL Server) for datasources for on-premise deployment on an Agent.
- Data Quality now enables cross-connection within the same account across cloud environments, for Snowflake and Databricks platforms.