Best practices for Reltio Data Sharing with Databricks
Learn about best practices for using Reltio Data Sharing with Databricks so that you can choose the recommended compute type, query the supported tables and views, and avoid unsupported downstream usage.
Recommended practices
- Use Serverless Compute to read shared materialized views
- Use Serverless Compute to read data from the materialized views that are shared through data sharing. This provides optimal read performance.
For more information on materialized views, see Direct access to materialized views.
- Use data shares for analytics and data engineering workloads
- Use data shares for downstream systems such as BI, reporting, and ML pipelines.
- Query supported streaming tables
- Use the following streaming tables to query data.
activitieslinksmatchesmergesworkflows
- Query supported materialized views
- Use the following materialized views to query data.
entity_<entity_type>relation_<relation_type>interaction_<interaction_type>
Practices to avoid
- Avoid using non-dedicated classic compute for shared materialized views
- Do not use non-dedicated classic compute to read data from the materialized views shared through data sharing. This affects query read performance.
- Avoid querying landing and JSON tables
-
Do not use the following streaming tables to query data.
activities_landingtableentities_landingtableentities_jsonrelations_landingtablerelations_jsoninteractions_landingtableinteractions_jsonlinks_landingtablematches_landingtablemerges_landingtableworkflows_landingtable
- Avoid using data shares for non-analytics downstream systems
- Do not use data shares to build downstream systems for non-analytics use cases.