Supported Quality features by datasource - Precisely Data Integrity Suite

Data Integrity Suite

Product
Spatial_Analytics
Data_Integration
Data_Enrichment
Data_Governance
Precisely_Data_Integrity_Suite
geo_addressing_1
Data_Observability
Data_Quality
dis_core_foundation
Services
Spatial Analytics
Data Integration
Data Enrichment
Data Governance
Geo Addressing
Data Observability
Data Quality
Core Foundation
ft:title
Data Integrity Suite
ft:locale
en-US
PublicationType
pt_product_guide
copyrightfirst
2000
copyrightlast
2026

This table outlines the compatibility of various datasources with quality. Use this information to identify supported quality features by datasource.

Table 1.
Datasource Quality access and execution
  Deployment Method Pipeline Engine Sample Generation Run Default Rules Run Custom Rules Run Data Quality Pipelines Triggered cataloging on quality pipeline execution
Amazon S3 On-premise Precisely Agent Yes Yes Yes Yes Yes
BigQuery Cloud GCP Yes Yes Yes Yes Yes
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
SAP Hana On-premise Precisely Agent Yes Yes Yes Yes Yes
For more information, see Supported datasources. This documentation directs you to step-by-step instructions, including necessary parameters, and connection settings for each datasource.
Note:
  • Quality supports cross-connection (Oracle & SQL Server) for datasources in on-premise deployment on an Agent.
  • Quality enables cross-connection within the same account across cloud environments for Snowflake and Databricks platforms.