diff --git a/datafusion/common/src/dfschema.rs b/datafusion/common/src/dfschema.rs
index b7101e2bbf40..65bb40810f18 100644
--- a/datafusion/common/src/dfschema.rs
+++ b/datafusion/common/src/dfschema.rs
@@ -1034,7 +1034,7 @@ impl SchemaExt for Schema {
                 .iter()
                 .zip(other.fields().iter())
                 .try_for_each(|(f1, f2)| {
-                    if f1.name() != f2.name() || !DFSchema::datatype_is_logically_equal(f1.data_type(), f2.data_type()) {
+                    if f1.name() != f2.name() || (!DFSchema::datatype_is_logically_equal(f1.data_type(), f2.data_type()) && !can_cast_types(f2.data_type(), f1.data_type())) {
                         _plan_err!(
                             "Inserting query schema mismatch: Expected table field '{}' with type {:?}, \
                             but got '{}' with type {:?}.",
diff --git a/datafusion/core/tests/dataframe/mod.rs b/datafusion/core/tests/dataframe/mod.rs
index 43428d6846a1..a902cf8ae65b 100644
--- a/datafusion/core/tests/dataframe/mod.rs
+++ b/datafusion/core/tests/dataframe/mod.rs
@@ -5673,3 +5673,75 @@ async fn test_fill_null_all_columns() -> Result<()> {
     assert_batches_sorted_eq!(expected, &results);
     Ok(())
 }
+
+#[tokio::test]
+async fn test_insert_into_casting_support() -> Result<()> {
+    // Testing case1:
+    // Inserting query schema mismatch: Expected table field 'a' with type Float16, but got 'a' with type Utf8.
+    // And the cast is not supported from Utf8 to Float16.
+
+    // Create a new schema with one field called "a" of type Float16, and setting nullable to false
+    let schema = Arc::new(Schema::new(vec![Field::new("a", DataType::Float16, false)]));
+
+    let session_ctx = SessionContext::new();
+
+    // Create and register the initial table with the provided schema and data
+    let initial_table = Arc::new(MemTable::try_new(schema.clone(), vec![vec![]])?);
+    session_ctx.register_table("t", initial_table.clone())?;
+
+    let mut write_df = session_ctx.sql("values ('a123'), ('b456')").await.unwrap();
+
+    write_df = write_df
+        .clone()
+        .with_column_renamed("column1", "a")
+        .unwrap();
+
+    let e = write_df
+        .write_table("t", DataFrameWriteOptions::new())
+        .await
+        .unwrap_err();
+
+    assert_contains!(e.to_string(), "Inserting query schema mismatch: Expected table field 'a' with type Float16, but got 'a' with type Utf8.");
+
+    // Testing case2:
+    // Inserting query schema mismatch: Expected table field 'a' with type Utf8View, but got 'a' with type Utf8.
+    // And the cast is supported from Utf8 to Utf8View.
+
+    // Create a new schema with one field called "a" of type Utf8View, and setting nullable to false
+    let schema = Arc::new(Schema::new(vec![Field::new(
+        "a",
+        DataType::Utf8View,
+        false,
+    )]));
+
+    let initial_table = Arc::new(MemTable::try_new(schema.clone(), vec![vec![]])?);
+
+    session_ctx.register_table("t2", initial_table.clone())?;
+
+    let mut write_df = session_ctx.sql("values ('a123'), ('b456')").await.unwrap();
+
+    write_df = write_df
+        .clone()
+        .with_column_renamed("column1", "a")
+        .unwrap();
+
+    write_df
+        .write_table("t2", DataFrameWriteOptions::new())
+        .await?;
+
+    let res = session_ctx
+        .sql("select * from t2")
+        .await
+        .unwrap()
+        .collect()
+        .await
+        .unwrap();
+
+    // The result should be the same as the input which is ['a123', 'b456']
+    let expected = [
+        "+------+", "| a    |", "+------+", "| a123 |", "| b456 |", "+------+",
+    ];
+
+    assert_batches_eq!(expected, &res);
+    Ok(())
+}