diff --git a/examples/README.md b/examples/README.md
index 0e1d85e..007190f 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -36,6 +36,7 @@ Located in [`examples/integrations/`](integrations/):
 | **pandas** | [`pandas_workflow.py`](integrations/pandas_workflow.py) | Drop-in cleaning step within an existing pandas aggregation workflow. |
 | **Polars** | [`polars_workflow.py`](integrations/polars_workflow.py) | Native `Polars in -> Polars out` execution with downstream Polars query expressions. |
 | **DuckDB** | [`duckdb_workflow.py`](integrations/duckdb_workflow.py) | Direct cleaning of `DuckDBPyRelation` objects and registering back to DuckDB for SQL. |
+| **DuckDB Parquet** | [`duckdb_parquet_export.py`](integrations/duckdb_parquet_export.py) | Reading partitioned Parquet directories, cleaning with FreshData, and exporting back via DuckDB. |
 | **scikit-learn** | [`sklearn_pipeline.py`](integrations/sklearn_pipeline.py) | Custom `FreshDataCleaner` transformer for native `sklearn.pipeline.Pipeline` usage. |
 | **Apache Airflow** | [`airflow_task.py`](integrations/airflow_task.py) | Quality gating with `evaluate_trust_gate` and `FreshDataCleanOperator` DAG pattern. |
 | **Pandera** | [`09_pandera_recipe.py`](09_pandera_recipe.py) | Pre- and post-validation using declarative Pandera DataFrame schemas. |
diff --git a/examples/integrations/duckdb_parquet_export.py b/examples/integrations/duckdb_parquet_export.py
new file mode 100644
index 0000000..66b7a25
--- /dev/null
+++ b/examples/integrations/duckdb_parquet_export.py
@@ -0,0 +1,99 @@
+"""examples/integrations/duckdb_parquet_export.py — Partitioned Parquet Cleaning & Export.
+
+Problem:
+--------
+Analytical data pipelines often store large datasets as partitioned Parquet files
+(e.g., partitioned by region or year). Raw partitions often contain messy values,
+inconsistent types, and unstandardized strings. FreshData cleans the dataset, and
+DuckDB writes the cleaned result back out into a partitioned Parquet destination.
+
+Installation:
+-------------
+pip install "freshdata-cleaner[duckdb]"
+"""
+
+from __future__ import annotations
+
+import tempfile
+from pathlib import Path
+
+import duckdb
+
+import freshdata as fd
+
+
+def main() -> None:
+    print("=== Integration: DuckDB Partitioned Parquet Export & FreshData ===")
+
+    # 1. Manage DuckDB connection using a context manager
+    with duckdb.connect() as con:
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            base_path = Path(tmp_dir)
+            raw_parquet_dir = base_path / "raw_sales"
+            cleaned_parquet_dir = base_path / "cleaned_sales"
+
+            # Escape single quotes for SQL literal interpolation
+            raw_parquet_path = raw_parquet_dir.as_posix().replace("'", "''")
+            cleaned_parquet_path = cleaned_parquet_dir.as_posix().replace("'", "''")
+
+            # 2. Setup raw partitioned Parquet dataset
+            print("\n[1] Creating raw partitioned Parquet dataset...")
+            con.execute(f"""
+                CREATE TABLE raw_data AS SELECT * FROM (VALUES
+                    ('TX-101', 'North', '  Acme Corp  ', '$15,000.50', '2024-01-10'),
+                    ('TX-102', 'North', 'Beta Ltd', 'N/A', '2024-01-12'),
+                    ('TX-201', 'South', 'Gamma LLC', '$8,400.00', 'invalid_date'),
+                    ('TX-202', 'South', 'Delta Inc', '$22,100.00', '2024-02-01')
+                ) AS t(transaction_id, region, company_name, revenue, sale_date);
+
+                COPY raw_data TO '{raw_parquet_path}' (
+                    FORMAT PARQUET,
+                    PARTITION_BY (region),
+                    OVERWRITE_OR_IGNORE 1
+                );
+            """)
+
+            # 3. Read partitioned Parquet files directly into a DuckDB relation using glob pattern
+            print("\n[2] Reading partitioned Parquet dataset into DuckDB relation...")
+            raw_relation = con.read_parquet(
+                f"{raw_parquet_dir.as_posix()}/**/*.parquet",
+                hive_partitioning=True,
+            )
+            print(raw_relation.df())
+
+            # 4. Clean messy dataset using FreshData (materialize relation via fetchdf() to avoid catalog isolation issues)
+            print("\n[3] Cleaning data with FreshData...")
+            cleaned_df = fd.clean(
+                raw_relation.fetchdf(),
+                preserve_columns=["transaction_id", "region"],
+                strategy="conservative",
+            )
+            print(cleaned_df)
+
+            # 5. Register and re-export cleaned data back into partitioned Parquet destination
+            print("\n[4] Exporting cleaned data to partitioned Parquet using DuckDB...")
+            con.register("cleaned_table", cleaned_df)
+            con.execute(f"""
+                COPY (SELECT * FROM cleaned_table) TO '{cleaned_parquet_path}' (
+                    FORMAT PARQUET,
+                    PARTITION_BY (region),
+                    OVERWRITE_OR_IGNORE 1
+                );
+            """)
+
+            # 6. Verify row count and partition existence for self-validation
+            print("\n[5] Verifying exported Parquet files:")
+            verified_relation = con.read_parquet(
+                f"{cleaned_parquet_dir.as_posix()}/**/*.parquet",
+                hive_partitioning=True,
+            )
+            print(verified_relation.df())
+
+            assert len(verified_relation) == 4
+            assert (cleaned_parquet_dir / "region=North").exists()
+
+            print("\nSuccessfully exported cleaned partitioned dataset!")
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file