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Answer: The streaming side of the join controls the process; only new data on the streaming side triggers processing.
In a stream-static join, the streaming side drives the join process. This means only new data appearing on the streaming side will trigger processing. Adding records to the static table does not automatically update the results of the join. For more details, refer to the [Databricks documentation on stream-static joins](https://docs.databricks.com/structured-streaming/delta-lake.html#performing-stream-static-joins).
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A data engineer is working with a streaming job that updates a Delta table named 'user_activities' by joining a streaming Delta table 'activity_logs' with a static Delta table 'users'. They observed that adding new users to the 'users' table does not automatically update the 'user_activities' table, even when there are activities for those users in 'activity_logs'. What is the most likely explanation for this issue?
A
The 'users' table needs to be manually refreshed with the REFRESH TABLE command for each microbatch of the join.
B
Stream-static joins are not stateful by default unless the spark configuration 'delta.statefulStreamStaticJoin' is set to true.
C
The streaming side of the join controls the process; only new data on the streaming side triggers processing.
D
The static side of the join drives the process, but only in batch mode.
E
Delta Lake does not support joins between static and streaming tables.