@dbhavesh 

In DLT, you can achieve similar functionality to the ROW_NUMBER() function in SQL by using the ROW_NUMBER() window function within your DLT pipeline. This can be done using PySpark or SQL syntax within your DLT pipeline code.

Hi TakuyaOmi, thanks for your response.

I did try that out, but receiving this kind of error as shown in the image below:

Please let me know your thoughts.

Thanks in advance!

@dbhavesh 

I apologize for the lack of explanation.

The ROW_NUMBER function requires ordering over the entire dataset, making it a non-time-based window function. When applied to streaming data, it results in the "NON_TIME_WINDOW_NOT_SUPPORTED_IN_STREAMING" error.

This issue occurs specifically in DLT streaming tables because they continuously process incoming data. However, in the case of materialized views, data is processed as a snapshot at a given point in time, allowing ordering without triggering this error.

If you need to generate sequential numbers, consider either:

1. Using a materialized view instead of a streaming table, or
2. Defining an IDENTITY column in the table schema, which automatically assigns unique sequential numbers upon data insertion.*

* Databricks Documentation – Identity Columns in Delta Lake