topic Re: Difference between libraries dlt and dp in Data Engineering

Difference between libraries dlt and dp

yit — Thu, 09 Oct 2025 12:10:47 GMT

In all Databricks documentation, the examples use import dlt to create streaming tables and views. But, when generating sample Python code in ETL pipeline, the import in the sample is:

import pyspark import pipelines as dp

Which one is the correct library? Are there significant differences between these two?

I could not find any comparison between these two.

Re: Difference between libraries dlt and dp

szymon_dybczak — Thu, 09 Oct 2025 12:35:07 GMT

Hi @yit ,

Basically, they are the same thing. Not so long ago, they renamed DLT (Delta Live Tables) to Lakeflow Declartive Pipelines. So I think from now on in all materials you will see new name for this library: pipelines (instead of dlt)

guess they didn't managed to change old name in all their docs yet, so you can see here and there old name for that library.

Re: Difference between libraries dlt and dp

nayan_wylde — Thu, 09 Oct 2025 14:34:08 GMT

@yit Functionally, they are equivalent concepts (declarative definitions for streaming tables, materialized views, expectations, CDC, etc.). The differences you’ll notice are mostly naming/ergonomics:

Module name:
Databricks docs & most existing notebooks: import dlt

https://docs.databricks.com/aws/en/dlt/python-dev

Spark guide & some generated samples: from pyspark import pipelines as dp

https://spark.apache.org/docs/_site/declarative-pipelines-programming-guide.html