diff --git a/python/requirements.txt b/python/requirements.txt
index 81136fb0..d4b4859e 100644
--- a/python/requirements.txt
+++ b/python/requirements.txt
@@ -1,5 +1,5 @@
 Sphinx>=4.0.2
-pyarrow==15.0.2
+pyarrow==16.1.0
 pandas>=1.2.5
 opentelemetry-api>=1.0.0
-opentelemetry-sdk>=1.0.0
\ No newline at end of file
+opentelemetry-sdk>=1.0.0
diff --git a/python/source/flight.rst b/python/source/flight.rst
index 67c359c1..66b581eb 100644
--- a/python/source/flight.rst
+++ b/python/source/flight.rst
@@ -967,4 +967,126 @@ middleware, and one in the server middleware.
     :hide:
 
     # Shutdown the server
-    server.shutdown()
\ No newline at end of file
+    server.shutdown()
+
+Sending Mixed Schema Data over a single Flight Stream
+=====================================================
+
+In some applications it is desirable to transmit multiple record batches or tables with
+*different* schemas over a single Arrow Flight stream. Although the IPC stream
+specification does not allow for multiple schemas within a stream, it is possible to
+work around this limitation by embedding the mixed schema data within a
+:class:`pyarrow.UnionArray`.
+
+The folllowing example shows how to transmit two record batches with different schemas
+over a single Flight stream.
+
+.. testcode::
+
+    import pyarrow as pa
+
+    # Create record batches with different schemas
+    n_legs = pa.array([2, 2, 4, 4, 5, 100])
+    animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"])
+    animals_recbatch = pa.RecordBatch.from_arrays(
+        [n_legs, animals], names=["n_legs", "animals"],
+    )
+
+    years = pa.array([2020, 2022, 2021, 2022])
+    months = pa.array([3, 5, 7, 9])
+    days = pa.array([1, 5, 9, 13])
+    dates_recbatch = pa.RecordBatch.from_arrays(
+        [years, months, days], names=["year", "month", "day"],
+    )
+
+    # Convert the record batches to struct arrays
+    animals_array = animals_recbatch.to_struct_array()
+    dates_array = dates_recbatch.to_struct_array()
+
+    # Create a UnionArray containing the animals data followed by the dates data
+    n_animals, n_dates = animals_recbatch.num_rows, dates_recbatch.num_rows
+    types = pa.array(([0] * n_animals) + ([1] * n_dates), type=pa.int8())
+    offsets = pa.array(list(range(n_animals)) + list(range(n_dates)), type=pa.int32())
+    union_array = pa.UnionArray.from_dense(
+        types, offsets, [animals_array, dates_array], ['animals', 'dates'],
+    )
+
+    # Finally create a single column RecordBatch from the UnionArray
+    union_recbatch = pa.RecordBatch.from_arrays([union_array], ['animals_dates_union'])
+
+.. testcode::
+    :hide:
+
+    print(union_recbatch)
+
+.. testoutput::
+    :hide:
+
+    pyarrow.RecordBatch
+    animals_dates_union: dense_union<animals: struct<n_legs: int64, animals: string>=0, dates: struct<year: int64, month: int64, day: int64>=1>
+      child 0, animals: struct<n_legs: int64, animals: string>
+          child 0, n_legs: int64
+          child 1, animals: string
+      child 1, dates: struct<year: int64, month: int64, day: int64>
+          child 0, year: int64
+          child 1, month: int64
+          child 2, day: int64
+    ----
+    animals_dates_union: -- is_valid: all not null-- type_ids: [0,0,0,0,0,0,1,1,1,1]-- value_offsets: [0,1,2,3,4,5,0,1,2,3]
+    -- child 0 type: struct<n_legs: int64, animals: string>
+    -- is_valid: all not null
+    -- child 0 type: int64
+    [2,2,4,4,5,100]
+    -- child 1 type: string
+    ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]
+    -- child 1 type: struct<year: int64, month: int64, day: int64>
+    -- is_valid: all not null
+    -- child 0 type: int64
+    [2020,2022,2021,2022]
+    -- child 1 type: int64
+    [3,5,7,9]
+    -- child 2 type: int64
+    [1,5,9,13]
+
+We can then use :meth:`pyarrow.flight.FlightStreamWriter.write_batch` to write the
+"mixed schema" record batch to the Flight stream. On the client side we can reconstruct
+the original record batches as follows.
+
+.. testcode::
+    :hide:
+
+    chunk = (union_recbatch, None)
+
+.. testcode::
+
+    import pyarrow as pa
+
+    # Unpack chunk from FlightStreamReader.read_chunk()
+    data, metadata = chunk
+    union_array = data.column(0)
+    animals_recbatch = pa.RecordBatch.from_struct_array(union_array.field(0))
+    dates_recbatch = pa.RecordBatch.from_struct_array(union_array.field(1))
+
+.. testcode::
+    :hide:
+
+    print(animals_recbatch)
+    print(dates_recbatch)
+
+.. testoutput::
+    :hide:
+
+    pyarrow.RecordBatch
+    n_legs: int64
+    animals: string
+    ----
+    n_legs: [2,2,4,4,5,100]
+    animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]
+    pyarrow.RecordBatch
+    year: int64
+    month: int64
+    day: int64
+    ----
+    year: [2020,2022,2021,2022]
+    month: [3,5,7,9]
+    day: [1,5,9,13]