diff --git a/src/driutils/benchmarking/partitioned_date_queries.py b/src/driutils/benchmarking/partitioned_date_queries.py
new file mode 100644
index 0000000..ca25724
--- /dev/null
+++ b/src/driutils/benchmarking/partitioned_date_queries.py
@@ -0,0 +1,205 @@
+"""Script to benchmark parquet file ingress from s3 using duckdb queries.
+
+Two different bucket structures have been created for testing.
+
+(current format)
+'partitioned_date': cosmos-test/structure/dataset=dataset_type/date=YYYY-MM-DD/data.parquet
+(proposed format)
+'partitioned_date_site': cosmos-test/structure/dataset=dataset_type/site=site/date=YYYY-MM-DD/data.parquet
+
+User can select which structure to query.
+
+Each query profile is saved to ./profile.json. Final metrics are written to csv.
+"""
+
+import json
+import os
+
+import duckdb
+import polars as pl
+
+# User defined inputs
+BUCKET = "ukceh-fdri"
+PREFIX = "cosmos-test"
+DATASET = "PRECIP_1MIN_2024_LOOPED"
+OUTPUT_PROFILE = "profile.json"
+OUTPUT_CSV = "metrics.csv"
+# Select columns to filter. List to select some, empty to select all.
+COLUMNS = ["SITE_ID", "time", "P_INTENSITY_RT"]
+
+# Derived constants
+BASE_BUCKET_PATH = f"s3://{BUCKET}/{PREFIX}/partitioned_date"
+COLUMNS_SQL = ", ".join(COLUMNS) if isinstance(COLUMNS, list) else "*"
+
+
+def extract_metrics(profile: str | os.PathLike) -> pl.DataFrame:
+    """Extract the relevant metrics into a polars datframe.
+
+    Args:
+        profile: the saved query profile json.
+
+    Returns:
+        polars dataframes with required profile metrics.
+    """
+
+    with open(profile) as f:
+        p = json.load(f)
+
+    metrics = {}
+    metrics["query"] = p["query_name"]
+    metrics["total_elapsed_query_time_(s)"] = p["latency"]
+    metrics["rows_returned"] = p["rows_returned"]
+    metrics["result_set_size_(Mb)"] = p["result_set_size"] / 1048576
+    metrics["rows_scanned"] = p["cumulative_rows_scanned"]
+    metrics["cpu_time_(s)"] = p["cpu_time"]
+
+    return pl.DataFrame(metrics)
+
+
+def query_one_site_one_date(base_path, dataset):  # noqa: ANN001, ANN201
+    # Test a very small return with partition filter
+    return f"""SELECT {COLUMNS_SQL} FROM read_parquet('{base_path}/dataset={dataset}/*/*.parquet')
+            WHERE date='2023-09-27' AND SITE_ID='BUNNY'"""
+
+
+def query_multi_dates_using_conditionals_month(base_path, dataset):  # noqa: ANN001, ANN201
+    # Test larger and more complex query parameters
+    # Dates are filtered using conditionals
+    return f"""
+        SELECT {COLUMNS_SQL}
+        FROM read_parquet('{base_path}/dataset={dataset}/*/*.parquet')
+        WHERE date >= '2019-01-01' AND date <= '2019-01-31' AND SITE_ID='BUNNY'
+    """
+
+
+def query_multi_dates_using_conditionals_year(base_path, dataset):  # noqa: ANN001, ANN201
+    # Test larger and more complex query parameters
+    # Dates are filtered using conditionals
+    return f"""
+        SELECT {COLUMNS_SQL}
+        FROM read_parquet('{base_path}/dataset={dataset}/*/*.parquet')
+        WHERE date >= '2019-01-01' AND date <= '2019-12-31' AND SITE_ID='BUNNY'
+    """
+
+
+def query_multi_sites_and_multi_dates_using_conditionals_month(base_path, dataset):  # noqa: ANN001, ANN201
+    # Test larger and more complex query parameters
+    # Dates are filtered using conditionals
+    # Non partitioned column used
+    return f"""
+        SELECT {COLUMNS_SQL}
+        FROM read_parquet('{base_path}/dataset={dataset}/*/*.parquet')
+        WHERE date >= '2019-01-01' AND date <= '2019-01-31'
+        AND SITE_ID IN ('BUNNY', 'ALIC1')
+    """
+
+
+def query_multi_sites_and_multi_dates_using_conditionals_year(base_path, dataset):  # noqa: ANN001, ANN201
+    # Test larger and more complex query parameters
+    # Dates are filtered using conditionals
+    # Non partitioned column used
+    return f"""
+        SELECT {COLUMNS_SQL}
+        FROM read_parquet('{base_path}/dataset={dataset}/*/*.parquet')
+        WHERE date >= '2019-01-01' AND date <= '2019-12-31'
+        AND SITE_ID IN ('BUNNY', 'ALIC1')
+    """
+
+
+def query_multi_dates_using_hive_types_month(base_path, dataset):  # noqa: ANN001, ANN201
+    # Test larger and more complex query parameters
+    # Dates are hive types and filtered using BETWEEN
+    # Fields of type DATE automatically picked up by duckdb so no need to specify as a hive type
+    return f"""
+        SELECT {COLUMNS_SQL}
+        FROM read_parquet('{base_path}/dataset={dataset}/*/*.parquet')
+        WHERE date BETWEEN '2019-01-01' AND '2019-01-31' AND SITE_ID='BUNNY'
+    """
+
+
+def query_multi_dates_using_hive_types_year(base_path, dataset):  # noqa: ANN001, ANN201
+    # Test larger and more complex query parameters
+    # Dates are hive types and filtered using BETWEEN
+    # Fields of type DATE automatically picked up by duckdb so no need to specify as a hive type
+    return f"""
+        SELECT {COLUMNS_SQL}
+        FROM read_parquet('{base_path}/dataset={dataset}/*/*.parquet')
+        WHERE date BETWEEN '2019-01-01' AND '2019-12-31' AND SITE_ID='BUNNY'
+    """
+
+
+def query_multi_sites_and_multi_dates_using_hive_types_month(base_path, dataset):  # noqa: ANN001, ANN201
+    # Test larger and more complex query parameters
+    # Dates are hive types and filtered using BETWEEN
+    # Non partitioned column used
+    # Fields of type DATE automatically picked up by duckdb so no need to specify as a hive type
+    return f"""
+        SELECT {COLUMNS_SQL}
+        FROM read_parquet('{base_path}/dataset={dataset}/*/*.parquet')
+        WHERE date BETWEEN '2019-01-01' AND '2019-01-31'
+        AND SITE_ID IN ('BUNNY', 'ALIC1')
+    """
+
+
+def query_multi_sites_and_multi_dates_using_hive_types_year(base_path, dataset):  # noqa: ANN001, ANN201
+    # Test larger and more complex query parameters
+    # Dates are hive types and filtered using BETWEEN
+    # Non partitioned column used
+    # Fields of type DATE automatically picked up by duckdb so no need to specify as a hive type
+    return f"""
+        SELECT {COLUMNS_SQL}
+        FROM read_parquet('{base_path}/dataset={dataset}/*/*.parquet')
+        WHERE date BETWEEN '2019-01-01' AND '2019-12-31'
+        AND SITE_ID IN ('BUNNY', 'ALIC1')
+    """
+
+
+if __name__ == "__main__":
+    # Setup basic duckdb connection
+    conn = duckdb.connect()
+
+    conn.execute("""
+        INSTALL httpfs;
+        LOAD httpfs;
+        SET force_download = true;
+        SET enable_profiling = json;
+        SET profiling_output = 'profile.json';
+    """)
+
+    # Add s3 connection details
+    conn.execute("""
+        CREATE SECRET aws_secret (
+            TYPE S3,
+            PROVIDER CREDENTIAL_CHAIN,
+            CHAIN 'sts'
+        );
+    """)
+
+    queries = [
+        query_one_site_one_date(BASE_BUCKET_PATH, DATASET),
+        query_multi_dates_using_conditionals_month(BASE_BUCKET_PATH, DATASET),
+        query_multi_dates_using_conditionals_year(BASE_BUCKET_PATH, DATASET),
+        query_multi_sites_and_multi_dates_using_conditionals_month(BASE_BUCKET_PATH, DATASET),
+        query_multi_sites_and_multi_dates_using_conditionals_year(BASE_BUCKET_PATH, DATASET),
+        query_multi_dates_using_hive_types_month(BASE_BUCKET_PATH, DATASET),
+        query_multi_dates_using_hive_types_year(BASE_BUCKET_PATH, DATASET),
+        query_multi_sites_and_multi_dates_using_hive_types_month(BASE_BUCKET_PATH, DATASET),
+        query_multi_sites_and_multi_dates_using_hive_types_year(BASE_BUCKET_PATH, DATASET),
+    ]
+
+    # Create empty dataframe to store the results
+    data = pl.DataFrame()
+
+    for query in queries:
+        print(f"Running {query}\n")
+
+        # Query profile is saved to ./profile.json
+        conn.execute(query).pl()
+
+        # Extract whats need from the profiler
+        df = extract_metrics(profile=OUTPUT_PROFILE)
+        print(df.glimpse())
+
+        data = pl.concat([data, df], how="diagonal")
+
+    data.write_csv(OUTPUT_CSV)
diff --git a/src/driutils/benchmarking/partitioned_date_site_queries.py b/src/driutils/benchmarking/partitioned_date_site_queries.py
new file mode 100644
index 0000000..6b46d49
--- /dev/null
+++ b/src/driutils/benchmarking/partitioned_date_site_queries.py
@@ -0,0 +1,205 @@
+"""Script to benchmark parquet file ingress from s3 using duckdb queries.
+
+Two different bucket structures have been created for testing.
+
+(current format)
+'partitioned_date': cosmos-test/structure/dataset=dataset_type/date=YYYY-MM-DD/data.parquet
+(proposed format)
+'partitioned_date_site': cosmos-test/structure/dataset=dataset_type/site=site/date=YYYY-MM-DD/data.parquet
+
+User can select which structure to query.
+
+Each query profile is saved to ./profile.json. Final metrics are written to csv.
+"""
+
+import json
+import os
+
+import duckdb
+import polars as pl
+
+# User defined inputs
+BUCKET = "ukceh-fdri"
+PREFIX = "cosmos-test"
+DATASET = "PRECIP_1MIN_2024_LOOPED"
+OUTPUT_PROFILE = "profile.json"
+OUTPUT_CSV = "metrics.csv"
+# Select columns to filter. List to select some, empty to select all.
+COLUMNS = ["SITE_ID", "time", "P_INTENSITY_RT"]
+
+# Derived constants
+BASE_BUCKET_PATH = f"s3://{BUCKET}/{PREFIX}/partitioned_date_site"
+COLUMNS_SQL = ", ".join(COLUMNS) if isinstance(COLUMNS, list) else "*"
+
+
+def extract_metrics(profile: str | os.PathLike) -> pl.DataFrame:
+    """Extract the relevant metrics into a polars datframe.
+
+    Args:
+        profile: the saved query profile json.
+
+    Returns:
+        polars dataframes with required profile metrics.
+    """
+
+    with open(profile) as f:
+        p = json.load(f)
+
+    metrics = {}
+    metrics["query"] = p["query_name"]
+    metrics["total_elapsed_query_time_(s)"] = p["latency"]
+    metrics["rows_returned"] = p["rows_returned"]
+    metrics["result_set_size_(Mb)"] = p["result_set_size"] / 1048576
+    metrics["rows_scanned"] = p["cumulative_rows_scanned"]
+    metrics["cpu_time_(s)"] = p["cpu_time"]
+
+    return pl.DataFrame(metrics)
+
+
+def query_one_site_one_date(base_path, dataset):  # noqa: ANN001, ANN201
+    # Test a very small return with partition filter
+    return f"""SELECT {COLUMNS_SQL} FROM read_parquet('{base_path}/dataset={dataset}/
+            site=BUNNY/date=2017-09-27/data.parquet')"""
+
+
+def query_multi_dates_using_conditionals_month(base_path, dataset):  # noqa: ANN001, ANN201
+    # Test larger and more complex query parameters
+    # Dates are filtered using conditionals
+    return f"""
+        SELECT {COLUMNS_SQL}
+        FROM read_parquet('{base_path}/dataset={dataset}/site=BUNNY/*/data.parquet')
+        WHERE date >= '2019-01-01' AND date <= '2019-01-31'
+    """
+
+
+def query_multi_dates_using_conditionals_year(base_path, dataset):  # noqa: ANN001, ANN201
+    # Test larger and more complex query parameters
+    # Dates are filtered using conditionals
+    return f"""
+        SELECT {COLUMNS_SQL}
+        FROM read_parquet('{base_path}/dataset={dataset}/site=BUNNY/*/data.parquet')
+        WHERE date >= '2019-01-01' AND date <= '2019-12-31'
+    """
+
+
+def query_multi_sites_and_multi_dates_using_conditionals_month(base_path, dataset):  # noqa: ANN001, ANN201
+    # Test larger and more complex query parameters
+    # Dates are filtered using conditionals
+    return f"""
+        SELECT {COLUMNS_SQL}
+        FROM read_parquet('{base_path}/dataset={dataset}/site=*/date=*/data.parquet')
+        WHERE date >= '2019-01-01' AND date <= '2019-01-31'
+        AND site IN ('BUNNY', 'ALIC1')
+    """
+
+
+def query_multi_sites_and_multi_dates_using_conditionals_year(base_path, dataset):  # noqa: ANN001, ANN201
+    # Test larger and more complex query parameters
+    # Dates are filtered using conditionals
+    return f"""
+        SELECT {COLUMNS_SQL}
+        FROM read_parquet('{base_path}/dataset={dataset}/site=*/date=*/data.parquet')
+        WHERE date >= '2019-01-01' AND date <= '2019-12-31'
+        AND site IN ('BUNNY', 'ALIC1')
+    """
+
+
+def query_multi_dates_using_hive_types_month(base_path, dataset):  # noqa: ANN001, ANN201
+    # Test larger and more complex query parameters
+    # Dates are hive types and filtered using BETWEEN
+    # Fields of type DATE automatically picked up by duckdb so no need to specify as a hive type
+    return f"""
+        SELECT {COLUMNS_SQL}
+        FROM read_parquet('{base_path}/dataset={dataset}/site=BUNNY/date=*/data.parquet')
+        WHERE date BETWEEN '2019-01-01' AND '2019-01-31'
+    """
+
+
+def query_multi_dates_using_hive_types_year(base_path, dataset):  # noqa: ANN001, ANN201
+    # Test larger and more complex query parameters
+    # Dates are hive types and filtered using BETWEEN
+    # Fields of type DATE automatically picked up by duckdb so no need to specify as a hive type
+    return f"""
+        SELECT {COLUMNS_SQL}
+        FROM read_parquet('{base_path}/dataset={dataset}/site=BUNNY/date=*/data.parquet')
+        WHERE date BETWEEN '2019-01-01' AND '2019-12-31'
+    """
+
+
+def query_multi_sites_and_multi_dates_using_hive_types_month(base_path, dataset):  # noqa: ANN001, ANN201
+    # Test larger and more complex query parameters
+    # Dates are hive types and filtered using BETWEEN
+    # Fields of type DATE automatically picked up by duckdb so no need to specify as a hive type
+    return f"""
+        SELECT {COLUMNS_SQL}
+        FROM read_parquet('{base_path}/dataset={dataset}/site=*/date=*/data.parquet')
+        WHERE date BETWEEN '2019-01-01' AND '2019-01-31'
+        AND site IN ('BUNNY', 'ALIC1')
+    """
+
+
+def query_multi_sites_and_multi_dates_using_hive_types_year(base_path, dataset):  # noqa: ANN001, ANN201
+    # Test larger and more complex query parameters
+    # Dates are hive types and filtered using BETWEEN
+    # Fields of type DATE automatically picked up by duckdb so no need to specify as a hive type
+    return f"""
+        SELECT {COLUMNS_SQL}
+        FROM read_parquet('{base_path}/dataset={dataset}/site=*/date=*/data.parquet')
+        WHERE date BETWEEN '2015-01-01' AND '2015-12-31'
+        AND site IN ('BUNNY', 'ALIC1')
+    """
+
+
+if __name__ == "__main__":
+    # Setup basic duckdb connection
+    conn = duckdb.connect(config={"threads": 64})
+
+    conn.execute("""
+        INSTALL httpfs;
+        LOAD httpfs;
+        SET force_download = false;
+        SET enable_profiling = json;
+        SET profiling_output = 'profile.json';
+    """)
+
+    # Add s3 connection details
+    conn.execute("""
+        CREATE SECRET aws_secret (
+            TYPE S3,
+            PROVIDER CREDENTIAL_CHAIN,
+            CHAIN 'sts'
+        );
+    """)
+
+    queries = [
+        query_one_site_one_date(BASE_BUCKET_PATH, DATASET),
+        query_multi_dates_using_conditionals_month(BASE_BUCKET_PATH, DATASET),
+        query_multi_dates_using_conditionals_year(BASE_BUCKET_PATH, DATASET),
+        query_multi_sites_and_multi_dates_using_conditionals_month(BASE_BUCKET_PATH, DATASET),
+        query_multi_sites_and_multi_dates_using_conditionals_year(BASE_BUCKET_PATH, DATASET),
+        query_multi_dates_using_hive_types_month(BASE_BUCKET_PATH, DATASET),
+        query_multi_dates_using_hive_types_year(BASE_BUCKET_PATH, DATASET),
+        query_multi_sites_and_multi_dates_using_hive_types_month(BASE_BUCKET_PATH, DATASET),
+        query_multi_sites_and_multi_dates_using_hive_types_year(BASE_BUCKET_PATH, DATASET),
+    ]
+
+    # Create empty dataframe to store the results
+    data = pl.DataFrame()
+
+    for query in queries:
+        print(f"Running {query}\n")
+
+        # Query profile is saved to ./profile.json
+        new_df = conn.sql(query).pl()
+
+        # Write out to csv to test all data returned
+        new_df.write_csv("./test.csv")
+
+        # Extract whats need from the profiler
+        df = extract_metrics(profile=OUTPUT_PROFILE)
+        print(df.glimpse())
+
+        data = pl.concat([data, df], how="diagonal")
+
+    conn.close()
+    data.write_csv(OUTPUT_CSV)