damondouglas commented on a change in pull request #12448:
URL: https://github.com/apache/beam/pull/12448#discussion_r464765533



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learning/katas/go/core_transforms/additional_parameters/additional_parameters/task.md
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+
+# Additional Parameters - Window and Timestamp
+
+This lesson introduces the concept of windowing and timestamped PCollection 
elements.
+Before discussing windowing, we need to distinguish bounded from unbounded 
data.
+Bounded data is of a fixed size such as a file or database query.  Unbounded 
data comes
+from a continuously updated source such as a subscription or stream.
+
+A window is a view into a fixed beginning and fixed end to a set of data.  In 
the beam model, windowing subdivides 
+a PCollection according to the timestamps of its individual elements.  This is 
useful
+for unbounded data because it allows the model to work with fixed element 
sizes.  Note that windowing
+is not unique to unbounded data.  The beam model windows all data whether it 
is bounded or unbounded.
+Yet, when you read from a fixed size source such as a file, beam applies the 
same timestamp to all the elements.

Review comment:
       My gut was telling me "beam applies the timestamp" was not quite it but 
now the way you describe it makes sense.




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