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databricks Platform · Updated Jun 23, 2026

Standalone pipeline materialized views support expectations

You can now add data quality expectations to materialized views created in Databricks SQL using the CONSTRAINT expectation name EXPECT (expectation expr) clause.

Update Open docs ↗release note ↗
What it does

You can now add data quality expectations to materialized views created in Databricks SQL using the CONSTRAINT expectation name EXPECT (expectation expr) clause. Expectations let you validate records as they flow through the standalone pipeline that backs the materialized view, and warn, drop, or fail on records that violate a constraint. Streaming tables created in Databricks SQL already support expectations.

Why it matters

Reduces operational overhead and unlocks new capabilities for data teams on Databricks.

Key capabilities
No expectations are defined on
No expectations are defined on a query.
A flow uses an operator
A flow uses an operator that does not support expectations.
The flow type, such as
The flow type, such as sinks, does not support expectations.
There are no updates to
There are no updates to the associated streaming table or materialized view for a given flow run.
Recommended use cases
For advanced use casesrecommended best practicessee Expectation recommendationsadvanced patterns.
Architecture
Databricks Platform → Platform
In short

You can now add data quality expectations to materialized views created in Databricks SQL using the CONSTRAINT expectation name EXPECT (expectation expr) clause. Expectations let you validate records as they flow through the standalone pipeline that backs…

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Key takeaway
You can now add data quality expectations to materialized views created in Databricks SQL using the CONSTRAINT expectation name EXPECT (expectation expr) clause.
2026-06-23-standalone-pipeline-materialized-views-support-expectations Generated by the Databricks Updates Agent · browse all updates