diff --git a/src/sdk/python/rtdip_sdk/pipelines/data_wranglers/spark/data_quality/interval_filtering.py b/src/sdk/python/rtdip_sdk/pipelines/data_wranglers/spark/data_quality/interval_filtering.py
new file mode 100644
index 000000000..b147c47d6
--- /dev/null
+++ b/src/sdk/python/rtdip_sdk/pipelines/data_wranglers/spark/data_quality/interval_filtering.py
@@ -0,0 +1,174 @@
+# Copyright 2022 RTDIP
+#
+# Licensed 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 datetime import timedelta
+
+import pandas as pd
+from pyspark.sql import functions as F
+from pyspark.sql import SparkSession
+from pyspark.sql import DataFrame
+
+from ...._pipeline_utils.models import Libraries, SystemType
+from ...interfaces import WranglerBaseInterface
+
+
+class IntervalFiltering(WranglerBaseInterface):
+    """
+    Cleanses a DataFrame by removing rows outside a specified interval window.
+    Example:
+
+
+    Parameters:
+        spark (SparkSession): A SparkSession object.
+        df (DataFrame): PySpark DataFrame to be converted
+        interval (int): The interval length for cleansing.
+         interval_unit (str): 'hours', 'minutes', 'seconds' or 'milliseconds' to specify the unit of the interval.
+    """
+
+    """ Default time stamp column name if not set in the constructor """
+    DEFAULT_TIME_STAMP_COLUMN_NAME: str = "EventTime"
+
+    def __init__(
+        self,
+        spark: SparkSession,
+        df: DataFrame,
+        interval: int,
+        interval_unit: str,
+        time_stamp_column_name: str = None,
+        tolerance: int = None,
+    ) -> None:
+        self.spark = spark
+        self.df = df
+        self.interval = interval
+        self.interval_unit = interval_unit
+        self.tolerance = tolerance
+        if time_stamp_column_name is None:
+            self.time_stamp_column_name = self.DEFAULT_TIME_STAMP_COLUMN_NAME
+        else:
+            self.time_stamp_column_name = time_stamp_column_name
+
+    @staticmethod
+    def system_type():
+        """
+        Attributes:
+            SystemType (Environment): Requires PYSPARK
+        """
+        return SystemType.PYSPARK
+
+    @staticmethod
+    def libraries():
+        libraries = Libraries()
+        return libraries
+
+    @staticmethod
+    def settings() -> dict:
+        return {}
+
+    def convert_column_to_timestamp(self) -> DataFrame:
+        try:
+            return self.df.withColumn(
+                self.time_stamp_column_name, F.to_timestamp(self.time_stamp_column_name)
+            )
+        except Exception as e:
+            raise ValueError(
+                f"Error converting column {self.time_stamp_column_name} to timestamp: {e}"
+            )
+
+    def get_time_delta(self, value: int) -> timedelta:
+        if self.interval_unit == "minutes":
+            return timedelta(minutes=value)
+        elif self.interval_unit == "days":
+            return timedelta(days=value)
+        elif self.interval_unit == "hours":
+            return timedelta(hours=value)
+        elif self.interval_unit == "seconds":
+            return timedelta(seconds=value)
+        elif self.interval_unit == "milliseconds":
+            return timedelta(milliseconds=value)
+        else:
+            raise ValueError(
+                "interval_unit must be either 'days', 'hours', 'minutes', 'seconds' or 'milliseconds'"
+            )
+
+    def check_if_outside_of_interval(
+        self,
+        current_time_stamp: pd.Timestamp,
+        last_time_stamp: pd.Timestamp,
+        time_delta_in_ms: float,
+        tolerance_in_ms: float,
+    ) -> bool:
+        if tolerance_in_ms is None:
+            return (
+                (current_time_stamp - last_time_stamp).total_seconds() * 1000
+            ) >= time_delta_in_ms
+        else:
+            return (
+                (current_time_stamp - last_time_stamp).total_seconds() * 1000
+            ) + tolerance_in_ms >= time_delta_in_ms
+
+    def format_date_time_to_string(self, time_stamp: pd.Timestamp) -> str:
+        try:
+            return time_stamp.strftime("%Y-%m-%d %H:%M:%S.%f")[:-3]
+        except Exception as e:
+            raise ValueError(f"Error converting timestamp to string: {e}")
+
+    def filter(self) -> DataFrame:
+        """
+        Filters the DataFrame based on the interval
+        """
+
+        if self.time_stamp_column_name not in self.df.columns:
+            raise ValueError(
+                f"Column {self.time_stamp_column_name} not found in the DataFrame."
+            )
+
+        original_schema = self.df.schema
+        self.df = self.convert_column_to_timestamp().orderBy(
+            self.time_stamp_column_name
+        )
+
+        tolerance_in_ms = None
+        if self.tolerance is not None:
+            tolerance_in_ms = self.get_time_delta(self.tolerance).total_seconds() * 1000
+        print(tolerance_in_ms)
+
+        time_delta_in_ms = self.get_time_delta(self.interval).total_seconds() * 1000
+
+        rows = self.df.collect()
+        last_time_stamp = rows[0][self.time_stamp_column_name]
+        first_row = rows[0].asDict()
+        first_row[self.time_stamp_column_name] = self.format_date_time_to_string(
+            first_row[self.time_stamp_column_name]
+        )
+
+        cleansed_df = [first_row]
+
+        for i in range(1, len(rows)):
+            current_row = rows[i]
+            current_time_stamp = current_row[self.time_stamp_column_name]
+
+            if self.check_if_outside_of_interval(
+                current_time_stamp, last_time_stamp, time_delta_in_ms, tolerance_in_ms
+            ):
+                current_row_dict = current_row.asDict()
+                current_row_dict[self.time_stamp_column_name] = (
+                    self.format_date_time_to_string(
+                        current_row_dict[self.time_stamp_column_name]
+                    )
+                )
+                cleansed_df.append(current_row_dict)
+                last_time_stamp = current_time_stamp
+
+        result_df = self.spark.createDataFrame(cleansed_df, schema=original_schema)
+
+        return result_df
diff --git a/tests/sdk/python/rtdip_sdk/pipelines/data_wranglers/spark/data_quality/test_interval_filtering.py b/tests/sdk/python/rtdip_sdk/pipelines/data_wranglers/spark/data_quality/test_interval_filtering.py
new file mode 100644
index 000000000..7ad4944d9
--- /dev/null
+++ b/tests/sdk/python/rtdip_sdk/pipelines/data_wranglers/spark/data_quality/test_interval_filtering.py
@@ -0,0 +1,304 @@
+# Copyright 2022 RTDIP
+#
+# Licensed 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.
+import pytest
+
+from pyspark.sql import SparkSession
+from src.sdk.python.rtdip_sdk.pipelines.data_wranglers.spark.data_quality.interval_filtering import (
+    IntervalFiltering,
+)
+
+
+@pytest.fixture(scope="session")
+def spark_session():
+    return SparkSession.builder.master("local[2]").appName("test").getOrCreate()
+
+
+def test_interval_detection_easy(spark_session: SparkSession):
+    expected_df = spark_session.createDataFrame(
+        [
+            ("A2PS64V0J.:ZUX09R", "2024-01-02 03:49:45.000", "Good", "0.129999995"),
+            ("A2PS64V0J.:ZUX09R", "2024-01-02 07:53:11.000", "Good", "0.119999997"),
+            ("A2PS64V0J.:ZUX09R", "2024-01-02 11:56:42.000", "Good", "0.129999995"),
+            ("A2PS64V0J.:ZUX09R", "2024-01-02 16:00:12.000", "Good", "0.150000006"),
+            ("A2PS64V0J.:ZUX09R", "2024-01-02 20:03:46.000", "Good", "0.340000004"),
+        ],
+        ["TagName", "EventTime", "Status", "Value"],
+    )
+
+    df = spark_session.createDataFrame(
+        [
+            ("A2PS64V0J.:ZUX09R", "2024-01-02 03:49:45.000", "Good", "0.129999995"),
+            ("A2PS64V0J.:ZUX09R", "2024-01-02 07:53:11.000", "Good", "0.119999997"),
+            ("A2PS64V0J.:ZUX09R", "2024-01-02 11:56:42.000", "Good", "0.129999995"),
+            ("A2PS64V0J.:ZUX09R", "2024-01-02 16:00:12.000", "Good", "0.150000006"),
+            ("A2PS64V0J.:ZUX09R", "2024-01-02 20:03:46.000", "Good", "0.340000004"),
+        ],
+        ["TagName", "EventTime", "Status", "Value"],
+    )
+
+    interval_filtering_wrangler = IntervalFiltering(
+        spark_session, df, 1, "seconds", "EventTime"
+    )
+    actual_df = interval_filtering_wrangler.filter()
+
+    assert expected_df.columns == actual_df.columns
+    assert expected_df.schema == actual_df.schema
+    assert expected_df.collect() == actual_df.collect()
+
+
+def test_interval_detection_easy_unordered(spark_session: SparkSession):
+    expected_df = spark_session.createDataFrame(
+        [
+            ("A2PS64V0J.:ZUX09R", "2024-01-02 03:49:45.000", "Good", "0.129999995"),
+            ("A2PS64V0J.:ZUX09R", "2024-01-02 07:53:11.000", "Good", "0.119999997"),
+            ("A2PS64V0J.:ZUX09R", "2024-01-02 11:56:42.000", "Good", "0.129999995"),
+            ("A2PS64V0J.:ZUX09R", "2024-01-02 16:00:12.000", "Good", "0.150000006"),
+            ("A2PS64V0J.:ZUX09R", "2024-01-02 20:03:46.000", "Good", "0.340000004"),
+        ],
+        ["TagName", "EventTime", "Status", "Value"],
+    )
+
+    df = spark_session.createDataFrame(
+        [
+            ("A2PS64V0J.:ZUX09R", "2024-01-02 07:53:11.000", "Good", "0.119999997"),
+            ("A2PS64V0J.:ZUX09R", "2024-01-02 16:00:12.000", "Good", "0.150000006"),
+            ("A2PS64V0J.:ZUX09R", "2024-01-02 03:49:45.000", "Good", "0.129999995"),
+            ("A2PS64V0J.:ZUX09R", "2024-01-02 11:56:42.000", "Good", "0.129999995"),
+            ("A2PS64V0J.:ZUX09R", "2024-01-02 20:03:46.000", "Good", "0.340000004"),
+        ],
+        ["TagName", "EventTime", "Status", "Value"],
+    )
+
+    interval_filtering_wrangler = IntervalFiltering(
+        spark_session, df, 1, "seconds", "EventTime"
+    )
+    actual_df = interval_filtering_wrangler.filter()
+
+    assert expected_df.columns == actual_df.columns
+    assert expected_df.schema == actual_df.schema
+    assert expected_df.collect() == actual_df.collect()
+
+
+def test_interval_detection_milliseconds(spark_session: SparkSession):
+    expected_df = spark_session.createDataFrame(
+        [
+            ("A2PS64V0JR", "2024-01-02 20:03:46.000"),
+            ("A2PS64asd.:ZUX09R", "2024-01-02 20:03:46.020"),
+            ("A2PS64asd.:ZUX09R", "2024-01-02 20:03:46.030"),
+        ],
+        ["TagName", "Time"],
+    )
+
+    df = spark_session.createDataFrame(
+        [
+            ("A2PS64V0JR", "2024-01-02 20:03:46.000"),
+            ("A2PS64asd.:ZUX09R", "2024-01-02 20:03:46.020"),
+            ("A2PS64V0J.:ZUX09R", "2024-01-02 20:03:46.025"),
+            ("A2PS64asd.:ZUX09R", "2024-01-02 20:03:46.030"),
+            ("A2PS64V0J.:ZUasdX09R", "2024-01-02 20:03:46.035"),
+        ],
+        ["TagName", "Time"],
+    )
+
+    interval_filtering_wrangler = IntervalFiltering(
+        spark_session, df, 10, "milliseconds", "Time"
+    )
+    actual_df = interval_filtering_wrangler.filter()
+
+    assert expected_df.columns == actual_df.columns
+    assert expected_df.schema == actual_df.schema
+    assert expected_df.collect() == actual_df.collect()
+
+
+def test_interval_detection_minutes(spark_session: SparkSession):
+    expected_df = spark_session.createDataFrame(
+        [
+            ("A2PS64V0JR", "2024-01-02 20:03:46.000"),
+            ("A2PS64asd.:ZUX09R", "2024-01-02 20:06:46.000"),
+            ("A2PS64asd.:ZUX09R", "2024-01-02 20:12:46.030"),
+        ],
+        ["TagName", "Time"],
+    )
+
+    df = spark_session.createDataFrame(
+        [
+            ("A2PS64V0JR", "2024-01-02 20:03:46.000"),
+            ("A2PS64asd.:ZUX09R", "2024-01-02 20:06:46.000"),
+            ("A2PS64V0J.:ZUX09R", "2024-01-02 20:09:45.999"),
+            ("A2PS64asd.:ZUX09R", "2024-01-02 20:12:46.030"),
+            ("A2PS64V0J.:ZUasdX09R", "2024-01-02 20:03:46.035"),
+        ],
+        ["TagName", "Time"],
+    )
+
+    interval_filtering_wrangler = IntervalFiltering(
+        spark_session, df, 3, "minutes", "Time"
+    )
+    actual_df = interval_filtering_wrangler.filter()
+
+    assert expected_df.columns == actual_df.columns
+    assert expected_df.schema == actual_df.schema
+    assert expected_df.collect() == actual_df.collect()
+
+
+def test_interval_detection_hours(spark_session: SparkSession):
+    expected_df = spark_session.createDataFrame(
+        [
+            ("A2PS64V0JR", "2024-01-02 20:03:46.000"),
+            ("A2PS64asd.:ZUX09R", "2024-01-02 21:06:46.000"),
+            ("A2PS64V0J.:ZUasdX09R", "2024-01-02 23:03:46.035"),
+        ],
+        ["TagName", "EventTime"],
+    )
+
+    df = spark_session.createDataFrame(
+        [
+            ("A2PS64V0JR", "2024-01-02 20:03:46.000"),
+            ("A2PS64asd.:ZUX09R", "2024-01-02 21:06:46.000"),
+            ("A2PS64V0J.:ZUX09R", "2024-01-02 21:09:45.999"),
+            ("A2PS64asd.:ZUX09R", "2024-01-02 21:12:46.030"),
+            ("A2PS64V0J.:ZUasdX09R", "2024-01-02 23:03:46.035"),
+        ],
+        ["TagName", "EventTime"],
+    )
+
+    interval_filtering_wrangler = IntervalFiltering(spark_session, df, 1, "hours")
+    actual_df = interval_filtering_wrangler.filter()
+
+    assert expected_df.columns == actual_df.columns
+    assert expected_df.schema == actual_df.schema
+    assert expected_df.collect() == actual_df.collect()
+
+
+def test_interval_detection_days(spark_session: SparkSession):
+    expected_df = spark_session.createDataFrame(
+        [
+            ("A2PS64V0JR", "2024-01-02 20:03:46.000"),
+            ("A2PS64asd.:ZUX09R", "2024-01-03 21:03:46.000"),
+            ("A2PS64asd.:ZUX09R", "2024-01-04 21:12:46.030"),
+            ("A2PS64V0J.:ZUasdX09R", "2028-01-01 23:03:46.035"),
+        ],
+        ["TagName", "EventTime"],
+    )
+
+    df = spark_session.createDataFrame(
+        [
+            ("A2PS64V0JR", "2024-01-02 20:03:46.000"),
+            ("A2PS64asd.:ZUX09R", "2024-01-03 21:03:46.000"),
+            ("A2PS64V0J.:ZUX09R", "2024-01-04 21:03:45.999"),
+            ("A2PS64asd.:ZUX09R", "2024-01-04 21:12:46.030"),
+            ("A2PS64V0J.:ZUasdX09R", "2028-01-01 23:03:46.035"),
+        ],
+        ["TagName", "EventTime"],
+    )
+
+    interval_filtering_wrangler = IntervalFiltering(spark_session, df, 1, "days")
+    actual_df = interval_filtering_wrangler.filter()
+
+    assert expected_df.columns == actual_df.columns
+    assert expected_df.schema == actual_df.schema
+    assert expected_df.collect() == actual_df.collect()
+
+
+def test_interval_detection_wrong_time_stamp_column_name(spark_session: SparkSession):
+    df = spark_session.createDataFrame(
+        [
+            ("A2PS64V0JR", "2024-01-02 20:03:46.000"),
+            ("A2PS64asd.:ZUX09R", "2024-01-02 21:06:46.000"),
+            ("A2PS64V0J.:ZUX09R", "2024-01-02 21:09:45.999"),
+            ("A2PS64asd.:ZUX09R", "2024-01-02 21:12:46.030"),
+            ("A2PS64V0J.:ZUasdX09R", "2024-01-02 23:03:46.035"),
+        ],
+        ["TagName", "EventTime"],
+    )
+
+    interval_filtering_wrangler = IntervalFiltering(
+        spark_session, df, 1, "hours", "Time"
+    )
+
+    with pytest.raises(ValueError):
+        interval_filtering_wrangler.filter()
+
+
+def test_interval_detection_wrong_interval_unit_pass(spark_session: SparkSession):
+    df = spark_session.createDataFrame(
+        [
+            ("A2PS64V0JR", "2024-01-02 20:03:46.000"),
+            ("A2PS64asd.:ZUX09R", "2024-01-02 21:06:46.000"),
+            ("A2PS64V0J.:ZUX09R", "2024-01-02 21:09:45.999"),
+            ("A2PS64asd.:ZUX09R", "2024-01-02 21:12:46.030"),
+            ("A2PS64V0J.:ZUasdX09R", "2024-01-02 23:03:46.035"),
+        ],
+        ["TagName", "EventTime"],
+    )
+
+    interval_filtering_wrangler = IntervalFiltering(
+        spark_session, df, 1, "years", "EventTime"
+    )
+
+    with pytest.raises(ValueError):
+        interval_filtering_wrangler.filter()
+
+
+def test_interval_detection_faulty_time_stamp(spark_session: SparkSession):
+    df = spark_session.createDataFrame(
+        [
+            ("A2PS64V0JR", "2024-01-09-02 20:03:46.000"),
+            ("A2PS64asd.:ZUX09R", "2024-01-02 21:06:46.000"),
+            ("A2PS64V0J.:ZUX09R", "2024-01-02 21:09:45.999"),
+            ("A2PS64asd.:ZUX09R", "2024-01-02 21:12:46.030"),
+            ("A2PS64V0J.:ZUasdX09R", "2024-01-02 23:03:46.035"),
+        ],
+        ["TagName", "EventTime"],
+    )
+
+    interval_filtering_wrangler = IntervalFiltering(
+        spark_session, df, 1, "minutes", "EventTime"
+    )
+
+    with pytest.raises(ValueError):
+        interval_filtering_wrangler.filter()
+
+
+def test_interval_tolerance(spark_session: SparkSession):
+    expected_df = spark_session.createDataFrame(
+        [
+            ("A2PS64V0J.:ZUX09R", "2024-01-02 03:49:45.000", "Good", "0.129999995"),
+            ("A2PS64V0J.:ZUX09R", "2024-01-02 03:49:47.000", "Good", "0.129999995"),
+            ("A2PS64V0J.:ZUX09R", "2024-01-02 03:49:50.000", "Good", "0.129999995"),
+            ("A2PS64V0J.:ZUX09R", "2024-01-02 03:49:52.000", "Good", "0.129999995"),
+        ],
+        ["TagName", "EventTime", "Status", "Value"],
+    )
+
+    df = spark_session.createDataFrame(
+        [
+            ("A2PS64V0J.:ZUX09R", "2024-01-02 03:49:45.000", "Good", "0.129999995"),
+            ("A2PS64V0J.:ZUX09R", "2024-01-02 03:49:46.000", "Good", "0.129999995"),
+            ("A2PS64V0J.:ZUX09R", "2024-01-02 03:49:47.000", "Good", "0.129999995"),
+            ("A2PS64V0J.:ZUX09R", "2024-01-02 03:49:50.000", "Good", "0.129999995"),
+            ("A2PS64V0J.:ZUX09R", "2024-01-02 03:49:51.000", "Good", "0.129999995"),
+            ("A2PS64V0J.:ZUX09R", "2024-01-02 03:49:52.000", "Good", "0.129999995"),
+        ],
+        ["TagName", "EventTime", "Status", "Value"],
+    )
+
+    interval_filtering_wrangler = IntervalFiltering(
+        spark_session, df, 3, "seconds", "EventTime", 1
+    )
+    actual_df = interval_filtering_wrangler.filter()
+
+    assert expected_df.columns == actual_df.columns
+    assert expected_df.schema == actual_df.schema
+    assert expected_df.collect() == actual_df.collect()