gruuya commented on code in PR #825:
URL: https://github.com/apache/iceberg-rust/pull/825#discussion_r1894571153


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crates/integration_tests/testdata/pyiceberg/load_types_table.py:
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@@ -0,0 +1,79 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+from datafusion import SessionContext
+from pyiceberg.catalog import load_catalog
+import pyarrow.parquet as pq
+
+# Generate a table with various types in memory and dump to a Parquet file
+ctx = SessionContext()

Review Comment:
   > curious to hear about what you guys ran into.
   
   Actually the only one so far seems to be this Iceberg-from-parquet creation 
discussed above—if the Parquet file has e.g. Int32 field with Int16 type hints 
spark will automatically coerce those in the resulting data file into Int32, 
whereas pyiceberg will leave it as is (and so during the scan in the actual 
record batches we get Int16, whilst the schema says it's Int32).



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