Materialized Views
Database objects that precompute and persistently store query results. They can be refreshed on a schedule or incrementally, and are used to reduce query latency and computational cost.
Timeline (last 90 days)
Databricks AI Search now natively supports Streaming Tables, Materialized Views and Managed Tables (based on Iceberg).
Databricks AI Search's support for Streaming Tables and Materialized Views is native, rather than pieced together through external connectors or custom ETL pipelines.
Materialized Views are database objects that precompute and persistently store query results; in Databricks they are typically defined by SQL and can be refreshed on a schedule or incrementally.
Teams using the native support still need to pay attention to: the streaming write frequency and index update strategy must match the business freshness requirements; the refresh cycle of materialized views affects the real-time nature of query results; and Iceberg table metadata management may bring additional overhead as the number of tables grows.
All Facts (4)
Databricks AI Search's support for Streaming Tables and Materialized Views is native, rather than pieced together through external connectors or custom ETL pipelines.
50%UnverifiedMaterialized Views are database objects that precompute and persistently store query results; in Databricks they are typically defined by SQL and can be refreshed on a schedule or incrementally.
50%UnverifiedTeams using the native support still need to pay attention to: the streaming write frequency and index update strategy must match the business freshness requirements; the refresh cycle of materialized views affects the real-time nature of query results; and Iceberg table metadata management may bring additional overhead as the number of tables grows.
50%UnverifiedDatabricks AI Search now natively supports Streaming Tables, Materialized Views and Managed Tables (based on Iceberg).
50%