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databricks Platform · Updated May 26, 2026

Lakeflow Spark Declarative Pipelines parameters are now in Beta

Pipeline parameters let you define key value pairs at the pipeline level and reference them from SQL source code using named parameter syntax.

Beta Open docs ↗release note ↗
What it does

Pipeline parameters let you define key value pairs at the pipeline level and reference them from SQL source code using named parameter syntax. Pipeline parameters let you reuse the same pipeline source code across environments or datasets. For example, you can run the same transformations against dev and prod catalogs, or ingest from a different source path on each run.

Why it matters

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

Key capabilities
A job that contains a
A job that contains a pipeline task and is configured with maxconcurrentruns more than one.
A pipeline task wrapped in
A pipeline task wrapped in a for-each task, regardless of the iteration count.
Architecture
Databricks Platform → Platform
In short

Pipeline parameters let you define key value pairs at the pipeline level and reference them from SQL source code using named parameter syntax. Pipeline parameters let you reuse the same pipeline source code across environments or datasets.

Read the full documentation ↗
Key takeaway
Pipeline parameters let you define key value pairs at the pipeline level and reference them from SQL source code using named parameter syntax.
2026-05-26-lakeflow-spark-declarative-pipelines-parameters-are-now-in-beta Generated by the Databricks Updates Agent · browse all updates