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DLT pipeline - silver table, joining streaming data



I'm trying to do my modeling in DLT pipelines. For bronze, I created 3 streaming views. When I try to join them to create silver table, I got an error that I can't join stream and stream without watermarks. I tried adding them but then I got no data. Does anyone know how to add watermarks to get all necessary data or is it possible to do it without watermarks?


New Contributor III
New Contributor III

Hello @ksenija ,


Streaming uses watermarks to control the threshold for how long to continue processing updates for a given state entity. Common examples of state entities include:

  • Aggregations over a time window.

  • Unique keys in a join between two streams.

When you declare a watermark, you specify a timestamp field and a watermark threshold on a streaming DataFrame. As new data arrives, the state manager tracks the most recent timestamp in the specified field and processes all records within the lateness threshold.

The following example applies a 10 minute watermark threshold to a windowed count:

from pyspark.sql.functions import window

  .withWatermark("event_time", "10 minutes")
    window("event_time", "5 minutes"),

In this example:

  • The event_time column is used to define a 10 minute watermark and a 5 minute tumbling window.

  • A count is collected for each id observed for each non-overlapping 5 minute windows.

  • State information is maintained for each count until the end of window is 10 minutes older than the latest observed event_time.

You can read more about watermark here:



Hi Ravi,

Thanks! What would you suggest for daily import of data while using DLT pipeline? Using streaming tables with 1 day watermark or to use materialized view?

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