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To define a view in Python, apply the @temporary_view decorator. Like the @table decorator, you can use views in Lakeflow Declarative Pipelines for either static or streaming datasets.
Note
The older dlt module used the @view decorator to defiine a temporary view. Databricks recommends using the pyspark.pipelines module (imported as dp) and the @temporary_view decorator to define temporary views.
The following is the syntax for defining views with Python:
Syntax
from pyspark import pipelines as dp
@dp.view(
name="<name>",
comment="<comment>")
@dp.expect(...)
def <function-name>():
return (<query>)
Parameters
| Parameter | Type | Description |
|---|---|---|
| function | function |
Required. A function that returns an Apache Spark DataFrame or streaming DataFrame from a user-defined query. |
name |
str |
The table name. If not provided, defaults to the function name. |
comment |
str |
A description for the table. |