How do I create a Delta Live Tables pipeline using Python, and what are the key differences between Delta Live Tables Python APIs and typical Python scripts?
To create a Delta Live Tables pipeline using Python, you need to follow these steps:

1. Import the Delta Live Tables module (`dlt`) in your Python notebook.
2. Download the data you want to use in your pipeline.
3. Create tables from files in object storage using the `@dlt.table` decorator before any Python function definition that returns a Spark DataFrame.
4. Add tables from upstream datasets in the pipeline using `dlt.read()`.
5. Create tables with enriched data views by declaring tables with specific business logic.

The key differences between Delta Live Tables Python APIs and typical Python scripts are:

1. Delta Live Tables interprets the decorator functions from the `dlt` module and builds a dataflow graph instead of calling the functions that perform data ingestion and transformation.
2. By default, table names are defined by function names in the Python interface.
3. Delta Live Tables processes updates to pipelines as a series of dependency graphs, allowing you to declare highly enriched views that power dashboards, BI, and analytics.

To configure a pipeline that uses the notebook, follow the instructions in the "Create a pipeline" section of the documentation.