diff --git a/CHANGELOG.md b/CHANGELOG.md
index 14de0a7..fbaab6c 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,14 @@
 # Change Log
 All notable changes to this project will be documented in this file.
 
+## 2.1.5 - 2026-03
+ ### Runner
+ - Added multi-column partitioning support via `target_partition_column` accepting a list
+ - Added `date_column` config option to explicitly specify which column receives the info_date
+ - Added `filters` option to dependencies for filtering by partition column values
+ - Added `target_table` config option to override the target table name
+ - Added `target_filters` config option for completion checking with multi-column partitions
+
 ## 2.1.4 - 2026-02
  ### Common
  - table reader optimization
diff --git a/README.md b/README.md
index 294522f..eedba73 100644
--- a/README.md
+++ b/README.md
@@ -32,14 +32,53 @@ This library currently contains:
 Runner is the orchestrator and scheduler of Rialto. It can be used to execute any [job](#jobs) but is primarily designed to execute feature [maker](#maker) jobs.
 The core of runner is execution of a Transformation class that can be extended for any purpose and the execution configuration that defines the handling of i/o, scheduling, dependencies and reporting.
 
+Runner operates on the assumption that your Databricks tables contain a date column (partition column) that indicates the date of data arrival. This enables:
+
+1. **Time-aware computation**: Run operations on dated tables while managing time-related dependencies automatically
+2. **Retrospective simulation**: Run computations as they would have occurred on a specific date by setting `run_date` - ensuring data newer than that date is never used
+3. **Dependency tracking**: Automatically verify that input data meets required freshness constraints relative to the run date
+4. **Automatic completion detection**: Skip computations when output data already exists (configurable with `rerun` parameter)
+
+#### Scheduling and Execution
+
+Runner uses a schedule-based approach:
+* Define a schedule (e.g., weekly on day 2, monthly on day 6) in the configuration
+* Specify a watch period (how far back to look for missing runs)
+* Runner finds all scheduled run dates within the watch period and executes missing ones
+* For each date, dependencies are checked and target existence is verified before execution
+
+#### Data Flow
+
+For each pipeline execution:
+1. **Dependency verification**: Check that all required input tables have data within specified time intervals
+2. **Transformation execution**: Run your transformation to produce a Spark DataFrame
+3. **Automatic enrichment**: Runner adds `INFORMATION_DATE` (the run date) and `VERSION` (package version) columns
+4. **Partitioned write**: Data is written to Databricks with partitioning configuration
+5. **Reporting**: Optional email notifications on failure and run information stored to tracking table
+
+#### Dependency Tracking
+
+Runner's dependency tracking ensures that all required input data is available before executing a pipeline. For each dependency:
+
+* **Date-based checking**: Runner looks for data in the dependency table's date column
+* **Interval calculation**: The required date is calculated by subtracting the dependency's interval from the run date
+* **Existence verification**: Runner checks if data exists for the calculated date (and within any specified filters)
+* **Missing data handling**: If required data is missing, Runner raises an error for that specific pipeline/date but continues executing other pipelines and dates in the queue
+
+**Example:** If you're running a pipeline on 2024-01-15 with a dependency that has a 7-day interval:
+* Runner checks if the dependency table has data for 2024-01-08 (15 days - 7 days)
+* If the dependency has `filters: {VERSION: "v2"}`, it specifically checks for data where VERSION='v2'
+* If data exists, the pipeline proceeds; otherwise, an error is raised for this specific execution, but other scheduled runs continue
+
+
 ### Transformation
 For the details on the interface see the [implementation](rialto/runner/transformation.py)
 Inside the transformation you have access to a [TableReader](#common), date of running, and if provided to Runner, a live spark session and [metadata manager](#metadata).
 You can either implement your jobs directly via extending the Transformation class, or by using the [jobs](#jobs) abstraction.
 
-### Runner
+### Basic Usage
 
-Bellow is the minimal code necessary to execute the runner.
+Below is the minimal code necessary to execute the runner.
 
 ```python
 from rialto.runner import Runner
@@ -64,6 +103,12 @@ Transformations are not included in the runner itself, it imports them dynamical
 
 ### Configuration
 
+Runner is supplied with a run configuration that defines the computations it will execute. In each pipeline configuration you define:
+- **Module**: Python transformation class to execute
+- **Schedule**: When to run (daily/weekly/monthly) and on which day
+- **Dependencies**: Input tables to check, with required freshness intervals and optional filters
+- **Target**: Output location, partitioning strategy, and optional completion filters
+
 ```yaml
 runner:
   watched_period_units: "months" # unit of default run period
@@ -97,12 +142,15 @@ pipelines: # a list of pipelines to run
         value: 1
     - table: catalog.schema.table2
       name: "table2"
+      date_col: info_date
       interval:
         units: "months"
         value: 1
   target:
       target_schema: catalog.schema # schema where tables will be created, must exist
-      target_partition_column: INFORMATION_DATE # date to partition new tables on
+      target_partition_column: INFORMATION_DATE # date to partition new tables on (can be a list for multi-column partitioning)
+      date_column: INFORMATION_DATE # Optional: explicitly specify which column receives the info_date value
+      target_table: custom_table_name # Optional: override the target table name (defaults to pipeline name)
   metadata_manager: # optional
       metadata_schema: catalog.metadata # schema where metadata is stored
   feature_loader: # optional
@@ -131,9 +179,102 @@ pipelines: # a list of pipelines to run
         value: 6
   target:
       target_schema: catalog.schema # schema where tables will be created, must exist
-      target_partition_column: INFORMATION_DATE # date to partition new tables on
+      target_partition_column: # can be a single column or list for multi-column partitioning
+        - INFORMATION_DATE
+        - VERSION
+```
+
+### Multi-Column Partitioning
+
+By default, Rialto partitions tables by a single date column (e.g., `INFORMATION_DATE`). However, you can partition by multiple columns to support use cases like A/B testing. For example, partitioning by `[INFORMATION_DATE, VERSION]` allows you to store multiple model versions side-by-side and track experiment variants.
+
+#### Basic Multi-Column Partitioning
+
+```yaml
+target:
+  target_schema: catalog.schema
+  target_partition_column:
+    - INFORMATION_DATE  # Date partition (receives run date automatically)
+    - VERSION           # Additional partition (must exist in transformation output)
+```
+
+#### Explicit Date Column
+
+If you want the date column to not be the first partition, specify it explicitly:
+
+```yaml
+target:
+  target_schema: catalog.schema
+  target_partition_column:
+    - VERSION             # Primary partition
+    - INFORMATION_DATE    # Secondary partition
+  date_column: INFORMATION_DATE  # Explicit: this column receives the run date
 ```
 
+#### Filtering Dependencies by Partition Values
+
+When working with multi-partitioned tables, you may need to ensure dependencies come from specific partition values. For example, when a multi-partitioned table is used as a dependency further in the pipeline, use `filters` to specify required values:
+
+```yaml
+dependencies:
+  - table: catalog.schema.features
+    date_col: INFORMATION_DATE
+    interval:
+      units: days
+      value: 7
+    filters:
+      VERSION: "v2"        # Only check for data where VERSION='v2'
+      REGION: "US"         # Can filter on multiple columns
+```
+
+#### Target Filters for Completion Checking
+
+When your target table is multi-partitioned, you need to specify which partition combination to check for completion:
+
+```yaml
+target:
+  target_schema: catalog.schema
+  target_partition_column:
+    - INFORMATION_DATE
+    - VERSION
+  target_filters:
+    VERSION: "v2"  # Only skip computation if v2 data exists
+```
+
+**Example scenario:**
+* Table has data for `INFORMATION_DATE=2024-01-01, VERSION=v1`
+* You want to compute `INFORMATION_DATE=2024-01-01, VERSION=v2`
+* Without `target_filters`: Runner sees the date exists → skips execution
+* With `target_filters`: Runner checks for v2 specifically → doesn't exist → runs computation
+
+
+### Using Filters in Transformations
+
+When you've defined dependency filters in your config, you should use the same filters when reading data in your transformation. The `TableReader.get_latest()` method supports a `filters` parameter:
+
+```python
+from rialto.runner import Transformation
+from rialto.runner.utils import find_dependency
+
+class MyTransformation(Transformation):
+    def run(self, reader, run_date, spark):
+        # Get dependency config including filters
+        dep = find_dependency(self.pipeline_config, "table1")
+
+        # Read with same filters used in dependency checking
+        df = reader.get_latest(
+            table=dep.table,
+            date_column=dep.date_col,
+            date_until=run_date,
+            filters=dep.filters  # Use filters from config
+        )
+
+        # Your transformation logic here
+        return df
+```
+
+### Runtime Parameterization
+
 The configuration can be dynamically overridden by providing a dictionary of overrides to the runner. All overrides must adhere to configurations schema, with pipeline.extras section available for custom schema.
 Here are few examples of overrides:
 
@@ -685,7 +826,17 @@ until = datetime.strptime("2020-01-01", "%Y-%m-%d").date()
 
 df = reader.get_latest(table="catalog.schema.table", date_until=until, date_column="information_date")
 
+# most recent partition with filters (for multi-partitioned tables)
+df = reader.get_latest(
+    table="catalog.schema.table",
+    date_column="information_date",
+    date_until=until,
+    filters={"VERSION": "v2", "REGION": "US"}  # Find latest within filtered subset
+)
 ```
+
+The `filters` parameter is particularly useful when working with multi-column partitioned tables. It ensures "latest" means the latest date within the filtered subset, not the latest date overall.
+
 For full information on parameters and their optionality see technical documentation.
 
 _TableReader_ needs an active spark session and an information which column is the **date column**.
diff --git a/pyproject.toml b/pyproject.toml
index b6c15cb..4f64923 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "rialto"
-version = "2.1.4"
+version = "2.1.5"
 description = "Rialto is a framework for building and deploying machine learning features in a scalable and reusable way. It provides a set of tools that make it easy to define and deploy features and models, and it provides a way to orchestrate the execution of these features and models."
 authors = [
     { name = "Marek Dobransky", email = "marekdobr@gmail.com" },
diff --git a/rialto/common/table_reader.py b/rialto/common/table_reader.py
index 16a6b38..0fa274a 100644
--- a/rialto/common/table_reader.py
+++ b/rialto/common/table_reader.py
@@ -16,7 +16,7 @@
 
 import abc
 import datetime
-from typing import Optional
+from typing import Dict, Optional
 
 import pyspark.sql.functions as F
 from pyspark.sql import DataFrame, SparkSession
@@ -37,6 +37,7 @@ def get_latest(
         date_column: str,
         date_until: Optional[datetime.date] = None,
         uppercase_columns: bool = False,
+        filters: Optional[Dict[str, str]] = None,
     ) -> DataFrame:
         """
         Get latest available date partition of the table until specified date
@@ -44,6 +45,7 @@ def get_latest(
         :param table: input table path
         :param date_until: Optional until date (inclusive)
         :param uppercase_columns: Option to refactor all column names to uppercase
+        :param filters: Optional dict of column filters to apply before finding latest date
         :return: Dataframe
         """
         raise NotImplementedError
@@ -102,6 +104,7 @@ def get_latest(
         date_column: str,
         date_until: Optional[datetime.date] = None,
         uppercase_columns: bool = False,
+        filters: Optional[Dict[str, str]] = None,
     ) -> DataFrame:
         """
         Get latest available date partition of the table until specified date
@@ -110,10 +113,15 @@ def get_latest(
         :param date_until: Optional until date (inclusive)
         :param date_column: column to filter dates on, takes highest priority
         :param uppercase_columns: Option to refactor all column names to uppercase
+        :param filters: Optional dict of column filters to apply before finding latest date
         :return: Dataframe
         """
         df = self.spark.read.table(table)
 
+        if filters:
+            for col, val in filters.items():
+                df = df.filter(df[col] == val)
+
         selected_date = self._get_latest_available_date(df, date_column, date_until)
         df = df.filter(F.col(date_column) == selected_date)
 
diff --git a/rialto/runner/config_loader.py b/rialto/runner/config_loader.py
index cc7c60b..e6b40ed 100644
--- a/rialto/runner/config_loader.py
+++ b/rialto/runner/config_loader.py
@@ -16,7 +16,7 @@
     "get_pipelines_config",
 ]
 
-from typing import Dict, List, Optional
+from typing import Dict, List, Optional, Union
 
 from pydantic import BaseModel, ConfigDict
 
@@ -44,6 +44,7 @@ class DependencyConfig(BaseConfig):
     name: Optional[str] = None
     date_col: str
     interval: IntervalConfig
+    filters: Optional[Dict[str, str]] = None
 
 
 class ModuleConfig(BaseConfig):
@@ -68,7 +69,10 @@ class RunnerConfig(BaseConfig):
 
 class TargetConfig(BaseConfig):
     target_schema: str
-    target_partition_column: str
+    target_partition_column: Union[str, List[str]]
+    date_column: Optional[str] = None
+    target_table: Optional[str] = None
+    target_filters: Optional[Dict[str, str]] = None
 
 
 class MetadataManagerConfig(BaseConfig):
diff --git a/rialto/runner/runner.py b/rialto/runner/runner.py
index 2807a77..b30b4ac 100644
--- a/rialto/runner/runner.py
+++ b/rialto/runner/runner.py
@@ -16,9 +16,8 @@
 
 import datetime
 from datetime import date
-from typing import Dict, List, Tuple
+from typing import Dict, List, Optional, Tuple
 
-import pyspark.sql.functions as F
 from loguru import logger
 from pyspark.sql import DataFrame, SparkSession
 
@@ -97,33 +96,49 @@ def _execute(self, instance: Transformation, run_date: date, pipeline: PipelineC
 
         return df
 
-    def _check_written(self, info_date: date, table: Table) -> int:
+    def _write_and_check(self, df: DataFrame, info_date: date, table: Table) -> int:
         """
-        Check if there are records written for given date
+        Write dataframe to storage and return number of rows written using Delta version tracking.
 
-        :param info_date: date to check
+        :param df: dataframe to write
+        :param info_date: date to partition
         :param table: target table object
-        :return: number of records
+        :return: number of records written, or 0 if write didn't happen
         """
-        df = self.spark.read.table(table.get_table_path())
-        df = df.filter(F.col(table.partition) == info_date)
-        return df.count()
+        table_path = table.get_table_path()
+        version_before = utils.get_table_version(self.spark, table_path)
 
-    def check_dates_have_partition(self, table: Table, dates: List[date]) -> List[bool]:
+        self.writer.write(df, info_date, table)
+
+        version_after = utils.get_table_version(self.spark, table_path)
+
+        if version_after is None:
+            return 0
+
+        if version_before is None or (version_after > version_before):
+            return utils.get_rows_from_history(self.spark, table_path, version_after)
+
+        return 0
+
+    def has_data_for_dates(
+        self, table: Table, dates: List[date], filters: Optional[Dict[str, str]] = None
+    ) -> List[bool]:
         """
-        For given list of dates, check if there is a matching partition for each
+        For given list of dates, check if there is matching data for each
 
         :param table: Table object
         :param dates: list of dates to check
+        :param filters: optional dict of partition column filters
         :return: list of bool
         """
-        if utils.table_exists(self.spark, table.get_table_path()):
-            partitions = utils.get_partitions(self.reader, table)
-            return [(date in partitions) for date in dates]
-        else:
+        if not utils.table_exists(self.spark, table.get_table_path()):
             logger.info(f"Table {table.get_table_path()} doesn't exist!")
             return [False for _ in dates]
 
+        all_dates = utils.get_available_dates(self.spark, table, filters)
+
+        return [(d in all_dates) for d in dates]
+
     def check_dependencies(self, pipeline: PipelineConfig, run_date: date) -> bool:
         """
         Check for all dependencies in config if they have available partitions
@@ -144,8 +159,8 @@ def check_dependencies(self, pipeline: PipelineConfig, run_date: date) -> bool:
 
             logger.debug(f"Date column for {dependency.table} is {dependency.date_col}")
 
-            source = Table(table_path=dependency.table, partition=dependency.date_col)
-            if True in self.check_dates_have_partition(source, possible_dep_dates):
+            source = Table(table_path=dependency.table, partitions=dependency.date_col)
+            if True in self.has_data_for_dates(source, possible_dep_dates, dependency.filters):
                 logger.info(f"Dependency for {dependency.table} from {dep_from} until {run_date} is fulfilled")
             else:
                 msg = f"Missing dependency for {dependency.table} from {dep_from} until {run_date}"
@@ -158,30 +173,36 @@ def check_dependencies(self, pipeline: PipelineConfig, run_date: date) -> bool:
 
         return True
 
-    def _get_completion(self, target: Table, info_dates: List[date]) -> List[bool]:
+    def _get_completion(
+        self, target: Table, info_dates: List[date], filters: Optional[Dict[str, str]] = None
+    ) -> List[bool]:
         """
         Check if model has run for given dates
 
-        :param target_path: Table object
+        :param target: Table object
         :param info_dates: list of dates
+        :param filters: optional dict of partition column filters for completion check
         :return: bool list
         """
         if self.rerun:
             return [False for _ in info_dates]
         else:
-            return self.check_dates_have_partition(target, info_dates)
+            return self.has_data_for_dates(target, info_dates, filters)
 
-    def _select_run_dates(self, pipeline: PipelineConfig, table: Table) -> Tuple[List, List]:
+    def _select_run_dates(
+        self, pipeline: PipelineConfig, table: Table, target_filters: Optional[Dict[str, str]] = None
+    ) -> Tuple[List, List]:
         """
         Select run dates and info dates based on completion
 
         :param pipeline: pipeline config
         :param table: table path
+        :param target_filters: optional dict of partition column filters for completion check
         :return: list of run dates and list of info dates
         """
         possible_run_dates = DateManager.run_dates(self.date_from, self.date_until, pipeline.schedule)
         possible_info_dates = [DateManager.to_info_date(x, pipeline.schedule) for x in possible_run_dates]
-        current_state = self._get_completion(table, possible_info_dates)
+        current_state = self._get_completion(table, possible_info_dates, target_filters)
 
         selection = [
             (run, info) for run, info, state in zip(possible_run_dates, possible_info_dates, current_state) if not state
@@ -211,8 +232,7 @@ def _run_one_date(self, pipeline: PipelineConfig, run_date: date, info_date: dat
 
             feature_group = utils.load_module(pipeline.module)
             df = self._execute(feature_group, run_date, pipeline)
-            self.writer.write(df, info_date, target)
-            records = self._check_written(info_date, target)
+            records = self._write_and_check(df, info_date, target)
             logger.info(f"Generated {records} records")
             if records == 0:
                 raise RuntimeError("No records generated")
@@ -229,12 +249,16 @@ def _run_pipeline(self, pipeline: PipelineConfig):
         """
         target = Table(
             schema_path=pipeline.target.target_schema,
+            table=pipeline.target.target_table,
             class_name=pipeline.module.python_class,
-            partition=pipeline.target.target_partition_column,
+            partitions=pipeline.target.target_partition_column,
+            date_column=pipeline.target.date_column,
         )
         logger.info(f"Loaded pipeline {pipeline.name}")
 
-        selected_run_dates, selected_info_dates = self._select_run_dates(pipeline, target)
+        selected_run_dates, selected_info_dates = self._select_run_dates(
+            pipeline, target, pipeline.target.target_filters
+        )
 
         # ----------- Checking dependencies available ----------
         for run_date, info_date in zip(selected_run_dates, selected_info_dates):
@@ -315,10 +339,14 @@ def debug(self) -> DataFrame:
 
         target = Table(
             schema_path=pipeline.target.target_schema,
+            table=pipeline.target.target_table,
             class_name=pipeline.module.python_class,
-            partition=pipeline.target.target_partition_column,
+            partitions=pipeline.target.target_partition_column,
+            date_column=pipeline.target.date_column,
+        )
+        selected_run_dates, selected_info_dates = self._select_run_dates(
+            pipeline, target, pipeline.target.target_filters
         )
-        selected_run_dates, selected_info_dates = self._select_run_dates(pipeline, target)
         if len(selected_run_dates) > 0:
             df = self._execute(utils.load_module(pipeline.module), selected_run_dates[0], pipeline)
             return self.writer._process(df, selected_info_dates[0], target)
diff --git a/rialto/runner/table.py b/rialto/runner/table.py
index 416bedb..e9433e7 100644
--- a/rialto/runner/table.py
+++ b/rialto/runner/table.py
@@ -14,6 +14,8 @@
 
 __all__ = ["Table"]
 
+from typing import List, Optional, Union
+
 from rialto.metadata import class_to_catalog_name
 
 
@@ -28,12 +30,29 @@ def __init__(
         schema_path: str = None,
         table_path: str = None,
         class_name: str = None,
-        partition: str = None,
+        partitions: Union[str, List[str]] = None,
+        date_column: Optional[str] = None,
     ):
         self.catalog = catalog
         self.schema = schema
         self.table = table
-        self.partition = partition
+
+        # Normalize partitions to list for consistent handling
+        if partitions is None:
+            self.partitions = []
+        elif isinstance(partitions, str):
+            self.partitions = [partitions]
+        else:
+            self.partitions = list(partitions)
+
+        # Date column: explicit or default to first partition
+        if date_column:
+            self.date_column = date_column
+        elif self.partitions:
+            self.date_column = self.partitions[0]
+        else:
+            self.date_column = None
+
         if schema_path:
             schema_path = schema_path.split(".")
             self.catalog = schema_path[0]
@@ -43,7 +62,7 @@ def __init__(
             self.catalog = table_path[0]
             self.schema = table_path[1]
             self.table = table_path[2]
-        if class_name:
+        if (self.table is None) and class_name:
             self.table = class_to_catalog_name(class_name)
 
     def get_schema_path(self):
diff --git a/rialto/runner/utils.py b/rialto/runner/utils.py
index 5af1723..fdfcf8c 100644
--- a/rialto/runner/utils.py
+++ b/rialto/runner/utils.py
@@ -12,15 +12,23 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 
-__all__ = ["load_module", "table_exists", "get_partitions", "init_tools", "find_dependency"]
+__all__ = [
+    "load_module",
+    "table_exists",
+    "get_table_version",
+    "get_available_dates",
+    "init_tools",
+    "find_dependency",
+    "get_rows_from_history",
+]
 
 from datetime import date
 from importlib import import_module
-from typing import List, Tuple
+from typing import Dict, List, Optional, Tuple
 
 from pyspark.sql import SparkSession
+from pyspark.sql import functions as F
 
-from rialto.common import DataReader
 from rialto.loader import PysparkFeatureLoader
 from rialto.metadata import MetadataManager
 from rialto.runner.config_loader import ModuleConfig, PipelineConfig
@@ -50,20 +58,47 @@ def table_exists(spark: SparkSession, table: str) -> bool:
     return spark.catalog.tableExists(table)
 
 
-def get_partitions(reader: DataReader, table: Table) -> List[date]:
+def get_table_version(spark: SparkSession, table: str) -> Optional[int]:
     """
-    Get partition values
+    Get the current Delta table version.
 
+    :param spark: SparkSession instance
+    :param table: full table path
+    :return: version number, or None if table doesn't exist
+    """
+    if not table_exists(spark, table):
+        return None
+    return spark.sql(f"DESCRIBE HISTORY {table}").first().version
+
+
+def get_available_dates(spark: SparkSession, table: Table, filters: Optional[Dict[str, str]] = None) -> List[date]:
+    """
+    Get distinct date values from table's partitions using SHOW PARTITIONS.
+
+    :param spark: SparkSession instance
     :param table: Table object
-    :return: List of partition values
+    :param filters: Optional dict of partition column filters to apply
+    :return: List of date values
     """
-    rows = (
-        reader.get_table(table.get_table_path(), date_column=table.partition)
-        .select(table.partition)
-        .distinct()
-        .collect()
-    )
-    return [r[table.partition] for r in rows]
+    partition_df = spark.sql(f"SHOW PARTITIONS {table.get_table_path()}")
+
+    if table.date_column not in partition_df.columns:
+        raise ValueError(
+            f"date_column '{table.date_column}' not found in partitions of {table.get_table_path()}. "
+            f"Available partition columns: {partition_df.columns}"
+        )
+
+    if filters:
+        for col, val in filters.items():
+            if col not in partition_df.columns:
+                raise ValueError(
+                    f"Filter column '{col}' not found in partitions of {table.get_table_path()}. "
+                    f"Available partition columns: {partition_df.columns}"
+                )
+            partition_df = partition_df.filter(F.col(col).cast("string") == val)
+
+    date_rows = partition_df.select(table.date_column).distinct().collect()
+    return [row[table.date_column] for row in date_rows]
 
 
 def init_tools(spark: SparkSession, pipeline: PipelineConfig) -> Tuple[MetadataManager, PysparkFeatureLoader]:
@@ -102,3 +137,21 @@ def find_dependency(config: PipelineConfig, name: str):
         if dep.name == name:
             return dep
     return None
+
+
+def get_rows_from_history(spark: SparkSession, table_path: str, version: str) -> int:
+    """
+    Get the number of rows written in a specific Delta commit version.
+
+    :param spark: SparkSession instance
+    :param table_path: Full table path (catalog.schema.table)
+    :param version: The commit version to look up
+    :return: Number of rows written, or 0 if unable to determine
+    """
+    history_df = spark.sql(f"DESCRIBE HISTORY {table_path}")
+    version_row = history_df.filter(history_df.version == version).first()
+
+    if version_row and version_row.operationMetrics.get("numOutputRows"):
+        return int(version_row.operationMetrics.get("numOutputRows"))
+
+    return 0
diff --git a/rialto/runner/writer.py b/rialto/runner/writer.py
index 3e31dac..b0a9305 100644
--- a/rialto/runner/writer.py
+++ b/rialto/runner/writer.py
@@ -68,7 +68,7 @@ def _align_schema(self, df: DataFrame, existing_columns: List) -> DataFrame:
         return df
 
     def _process(self, df: DataFrame, info_date: date, table: Table) -> DataFrame:
-        df = df.withColumn(table.partition, F.lit(info_date))
+        df = df.withColumn(table.date_column, F.lit(info_date))
 
         df = self._align_schema(df, self._get_existing_columns(table))
 
@@ -88,9 +88,9 @@ def write(self, df: DataFrame, info_date: date, table: Table) -> None:
         df = self._process(df, info_date, table)
 
         if self.merge_schema is True:
-            df.write.partitionBy(table.partition).mode("overwrite").option("mergeSchema", "true").saveAsTable(
+            df.write.partitionBy(*table.partitions).mode("overwrite").option("mergeSchema", "true").saveAsTable(
                 table.get_table_path()
             )
         else:
-            df.write.partitionBy(table.partition).mode("overwrite").saveAsTable(table.get_table_path())
-        logger.info(f"Results writen to {table.get_table_path()}")
+            df.write.partitionBy(*table.partitions).mode("overwrite").saveAsTable(table.get_table_path())
+        logger.info(f"Results written to {table.get_table_path()}")
diff --git a/tests/common/test_reader.py b/tests/common/test_reader.py
index c42b20b..b9a117a 100644
--- a/tests/common/test_reader.py
+++ b/tests/common/test_reader.py
@@ -12,6 +12,8 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 
+from datetime import date
+
 import pytest
 
 from rialto.common.table_reader import TableReader
@@ -27,7 +29,60 @@ def sample_df(spark):
     return df
 
 
+@pytest.fixture
+def multi_partition_df(spark):
+    df = spark.createDataFrame(
+        [
+            ("REGION_A", "TYPE_X", date(2023, 1, 1), 100),
+            ("REGION_A", "TYPE_X", date(2023, 1, 2), 200),
+            ("REGION_A", "TYPE_Y", date(2023, 1, 1), 150),
+            ("REGION_A", "TYPE_Y", date(2023, 1, 2), 250),
+            ("REGION_B", "TYPE_X", date(2023, 1, 1), 300),
+            ("REGION_B", "TYPE_X", date(2023, 1, 2), 400),
+        ],
+        schema="region string, type string, info_date date, value int",
+    )
+    return df
+
+
 def test_uppercase_columns(spark, sample_df):
     tr = TableReader(spark)
     df = tr._uppercase_column_names(sample_df)
     assert df.columns == ["A", "B", "C", "D", "E"]
+
+
+def test_get_latest_with_single_filter(multi_partition_df, mocker):
+    mock_spark = mocker.MagicMock()
+    mock_spark.read.table.return_value = multi_partition_df
+
+    tr = TableReader(mock_spark)
+    result = tr.get_latest("test_table", date_column="info_date", filters={"region": "REGION_A"})
+
+    assert result.count() == 2
+    assert all(row.info_date == date(2023, 1, 2) for row in result.collect())
+    assert all(row.region == "REGION_A" for row in result.collect())
+
+
+def test_get_latest_with_multiple_filters(multi_partition_df, mocker):
+    mock_spark = mocker.MagicMock()
+    mock_spark.read.table.return_value = multi_partition_df
+
+    tr = TableReader(mock_spark)
+    result = tr.get_latest("test_table", date_column="info_date", filters={"region": "REGION_A", "type": "TYPE_X"})
+
+    assert result.count() == 1
+    assert result.first().info_date == date(2023, 1, 2)
+    assert result.first().region == "REGION_A"
+    assert result.first().type == "TYPE_X"
+    assert result.first().value == 200
+
+
+def test_get_latest_without_filters(multi_partition_df, mocker):
+    mock_spark = mocker.MagicMock()
+    mock_spark.read.table.return_value = multi_partition_df
+
+    tr = TableReader(mock_spark)
+    result = tr.get_latest("test_table", date_column="info_date")
+
+    assert result.count() == 3
+    assert all(row.info_date == date(2023, 1, 2) for row in result.collect())
diff --git a/tests/runner/test_runner.py b/tests/runner/test_runner.py
index 352c555..f8bd251 100644
--- a/tests/runner/test_runner.py
+++ b/tests/runner/test_runner.py
@@ -11,61 +11,21 @@
 #  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 datetime
-from typing import Optional
-
 import pytest
-from pyspark.sql import DataFrame
 
 import rialto.runner.utils as utils
-from rialto.common.table_reader import DataReader
 from rialto.runner.runner import DateManager, Runner
 from rialto.runner.table import Table
-from tests.runner.runner_resources import (
-    dep1_data,
-    dep2_data,
-    general_schema,
-    simple_group_data,
-)
 from tests.runner.transformations.simple_group import SimpleGroup
 
 
-class MockReader(DataReader):
-    def __init__(self, spark):
-        self.spark = spark
-
-    def get_table(
-        self,
-        table: str,
-        date_from: Optional[datetime.date] = None,
-        date_to: Optional[datetime.date] = None,
-        date_column: str = None,
-        uppercase_columns: bool = False,
-    ) -> DataFrame:
-        if table == "catalog.schema.simple_group":
-            return self.spark.createDataFrame(simple_group_data, general_schema)
-        if table == "source.schema.dep1":
-            return self.spark.createDataFrame(dep1_data, general_schema)
-        if table == "source.schema.dep2":
-            return self.spark.createDataFrame(dep2_data, general_schema)
-
-    def get_latest(
-        self,
-        table: str,
-        date_until: Optional[datetime.date] = None,
-        date_column: str = None,
-        uppercase_columns: bool = False,
-    ) -> DataFrame:
-        pass
-
-
 def test_table_exists(spark, mocker):
     mock = mocker.patch("pyspark.sql.Catalog.tableExists", return_value=True)
     utils.table_exists(spark, "abc")
     mock.assert_called_once_with("abc")
 
 
-def test_load_module(spark, basic_runner):
+def test_load_module(basic_runner):
     module = utils.load_module(basic_runner.config.pipelines[0].module)
     assert isinstance(module, SimpleGroup)
 
@@ -123,45 +83,64 @@ def test_init_dates(spark):
     assert runner.date_until == DateManager.str_to_date("2023-03-31")
 
 
-def test_completion(spark, mocker, basic_runner):
+def test_completion(mocker, basic_runner):
     mocker.patch("rialto.runner.utils.table_exists", return_value=True)
-
-    basic_runner.reader = MockReader(spark)
+    mocker.patch(
+        "rialto.runner.utils.get_available_dates",
+        return_value=[
+            DateManager.str_to_date("2023-03-05"),
+            DateManager.str_to_date("2023-03-12"),
+            DateManager.str_to_date("2023-03-19"),
+        ],
+    )
 
     dates = ["2023-02-26", "2023-03-05", "2023-03-12", "2023-03-19", "2023-03-26"]
     dates = [DateManager.str_to_date(d) for d in dates]
 
-    comp = basic_runner._get_completion(Table(table_path="catalog.schema.simple_group", partition="DATE"), dates)
+    comp = basic_runner._get_completion(Table(table_path="catalog.schema.simple_group", partitions="DATE"), dates)
     expected = [False, True, True, True, False]
     assert comp == expected
 
 
-def test_completion_rerun(spark, mocker, basic_runner):
+def test_completion_rerun(spark, mocker):
     mocker.patch("rialto.runner.runner.utils.table_exists", return_value=True)
+    mocker.patch(
+        "rialto.runner.runner.utils.get_available_dates",
+        return_value=[
+            DateManager.str_to_date("2023-03-05"),
+            DateManager.str_to_date("2023-03-12"),
+            DateManager.str_to_date("2023-03-19"),
+        ],
+    )
 
     runner = Runner(spark, config_path="tests/runner/transformations/config.yaml", run_date="2023-03-31")
-    runner.reader = MockReader(spark)
 
     dates = ["2023-02-26", "2023-03-05", "2023-03-12", "2023-03-19", "2023-03-26"]
     dates = [DateManager.str_to_date(d) for d in dates]
 
-    comp = runner._get_completion(Table(table_path="catalog.schema.simple_group", partition="DATE"), dates)
+    comp = runner._get_completion(Table(table_path="catalog.schema.simple_group", partitions="DATE"), dates)
     expected = [False, True, True, True, False]
     assert comp == expected
 
 
 def test_check_dates_have_partition(spark, mocker):
     mocker.patch("rialto.runner.runner.utils.table_exists", return_value=True)
+    mocker.patch(
+        "rialto.runner.runner.utils.get_available_dates",
+        return_value=[
+            DateManager.str_to_date("2023-03-05"),
+        ],
+    )
 
     runner = Runner(
         spark,
         config_path="tests/runner/transformations/config.yaml",
         run_date="2023-03-31",
     )
-    runner.reader = MockReader(spark)
+
     dates = ["2023-03-04", "2023-03-05", "2023-03-06"]
     dates = [DateManager.str_to_date(d) for d in dates]
-    res = runner.check_dates_have_partition(Table(schema_path="source.schema", table="dep1", partition="DATE"), dates)
+    res = runner.has_data_for_dates(Table(schema_path="source.schema", table="dep1", partitions="DATE"), dates)
     expected = [False, True, False]
     assert res == expected
 
@@ -176,7 +155,7 @@ def test_check_dates_have_partition_no_table(spark, mocker):
     )
     dates = ["2023-03-04", "2023-03-05", "2023-03-06"]
     dates = [DateManager.str_to_date(d) for d in dates]
-    res = runner.check_dates_have_partition(Table(schema_path="source.schema", table="dep66", partition="DATE"), dates)
+    res = runner.has_data_for_dates(Table(schema_path="source.schema", table="dep66", partitions="DATE"), dates)
     expected = [False, False, False]
     assert res == expected
 
@@ -187,13 +166,19 @@ def test_check_dates_have_partition_no_table(spark, mocker):
 )
 def test_check_dependencies(spark, mocker, r_date, expected):
     mocker.patch("rialto.runner.runner.utils.table_exists", return_value=True)
+    mocker.patch(
+        "rialto.runner.runner.utils.get_available_dates",
+        return_value=[
+            DateManager.str_to_date("2023-03-05"),
+        ],
+    )
 
     runner = Runner(
         spark,
         config_path="tests/runner/transformations/config.yaml",
         run_date="2023-03-31",
     )
-    runner.reader = MockReader(spark)
+
     res = runner.check_dependencies(runner.config.pipelines[0], DateManager.str_to_date(r_date))
     assert res == expected
 
@@ -206,13 +191,20 @@ def test_check_no_dependencies(spark, mocker):
         config_path="tests/runner/transformations/config.yaml",
         run_date="2023-03-31",
     )
-    runner.reader = MockReader(spark)
     res = runner.check_dependencies(runner.config.pipelines[1], DateManager.str_to_date("2023-03-05"))
     assert res is True
 
 
 def test_select_dates(spark, mocker):
     mocker.patch("rialto.runner.runner.utils.table_exists", return_value=True)
+    mocker.patch(
+        "rialto.runner.runner.utils.get_available_dates",
+        return_value=[
+            DateManager.str_to_date("2023-03-05"),
+            DateManager.str_to_date("2023-03-12"),
+            DateManager.str_to_date("2023-03-19"),
+        ],
+    )
 
     runner = Runner(
         spark,
@@ -220,10 +212,9 @@ def test_select_dates(spark, mocker):
         run_date="2023-03-31",
         overrides={"runner.watched_period_units": "months", "runner.watched_period_value": 1},
     )
-    runner.reader = MockReader(spark)
 
     r, i = runner._select_run_dates(
-        runner.config.pipelines[0], Table(table_path="catalog.schema.simple_group", partition="DATE")
+        runner.config.pipelines[0], Table(table_path="catalog.schema.simple_group", partitions="DATE")
     )
     expected_run = ["2023-03-05", "2023-03-12", "2023-03-19", "2023-03-26"]
     expected_run = [DateManager.str_to_date(d) for d in expected_run]
@@ -235,6 +226,12 @@ def test_select_dates(spark, mocker):
 
 def test_select_dates_all_done(spark, mocker):
     mocker.patch("rialto.runner.runner.utils.table_exists", return_value=True)
+    mocker.patch(
+        "rialto.runner.runner.utils.get_available_dates",
+        return_value=[
+            DateManager.str_to_date("2023-03-02"),
+        ],
+    )
 
     runner = Runner(
         spark,
@@ -242,10 +239,9 @@ def test_select_dates_all_done(spark, mocker):
         run_date="2023-03-02",
         overrides={"runner.watched_period_units": "months", "runner.watched_period_value": 0},
     )
-    runner.reader = MockReader(spark)
 
     r, i = runner._select_run_dates(
-        runner.config.pipelines[0], Table(table_path="catalog.schema.simple_group", partition="DATE")
+        runner.config.pipelines[0], Table(table_path="catalog.schema.simple_group", partitions="DATE")
     )
     expected_run = []
     expected_run = [DateManager.str_to_date(d) for d in expected_run]
diff --git a/tests/runner/test_table.py b/tests/runner/test_table.py
index 82e6fa6..78ff2ba 100644
--- a/tests/runner/test_table.py
+++ b/tests/runner/test_table.py
@@ -26,3 +26,38 @@ def test_table_path_init():
     assert t.catalog == "cat"
     assert t.schema == "sch"
     assert t.table == "tab"
+
+
+def test_table_single_partition():
+    t = Table(catalog="cat", schema="sch", table="tab", partitions="DATE")
+
+    assert t.partitions == ["DATE"]
+    assert t.date_column == "DATE"
+
+
+def test_table_multiple_partitions():
+    t = Table(catalog="cat", schema="sch", table="tab", partitions=["REGION", "DATE"])
+
+    assert t.partitions == ["REGION", "DATE"]
+    assert t.date_column == "REGION"
+
+
+def test_table_multiple_partitions_with_date_column():
+    t = Table(catalog="cat", schema="sch", table="tab", partitions=["REGION", "DATE"], date_column="DATE")
+
+    assert t.partitions == ["REGION", "DATE"]
+    assert t.date_column == "DATE"
+
+
+def test_table_no_partitions():
+    t = Table(catalog="cat", schema="sch", table="tab")
+
+    assert t.partitions == []
+    assert t.date_column is None
+
+
+def test_table_explicit_date_column_only():
+    t = Table(catalog="cat", schema="sch", table="tab", date_column="CUSTOM_DATE")
+
+    assert t.partitions == []
+    assert t.date_column == "CUSTOM_DATE"
diff --git a/tests/runner/test_utils.py b/tests/runner/test_utils.py
new file mode 100644
index 0000000..1a968cc
--- /dev/null
+++ b/tests/runner/test_utils.py
@@ -0,0 +1,190 @@
+#  Copyright 2022 ABSA Group Limited
+#
+#  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
+
+import rialto.runner.utils as utils
+from rialto.runner.table import Table
+
+
+def test_get_table_version_nonexistent(mocker):
+    mock_spark = mocker.MagicMock()
+    mocker.patch("rialto.runner.utils.table_exists", return_value=False)
+
+    version = utils.get_table_version(mock_spark, "catalog.schema.nonexistent")
+
+    assert version is None
+
+
+def test_get_table_version_exists(spark, mocker):
+    mocker.patch("rialto.runner.utils.table_exists", return_value=True)
+
+    mock_history_df = spark.createDataFrame(
+        [(5, "2023-01-01", "user", "WRITE")],
+        schema="version int, timestamp string, userId string, operation string",
+    )
+
+    mock_spark = mocker.MagicMock()
+    mock_spark.sql.return_value = mock_history_df
+
+    version = utils.get_table_version(mock_spark, "catalog.schema.existing_table")
+
+    assert version == 5
+    mock_spark.sql.assert_called_once_with("DESCRIBE HISTORY catalog.schema.existing_table")
+
+
+def test_get_available_dates_without_filters(spark, mocker):
+    mock_partition_df = spark.createDataFrame(
+        [
+            ("2023-01-01",),
+            ("2023-01-02",),
+            ("2023-01-03",),
+        ],
+        schema="info_date string",
+    )
+
+    mock_spark = mocker.MagicMock()
+    mock_spark.sql.return_value = mock_partition_df
+
+    table = Table(catalog="cat", schema="sch", table="tab", partitions="info_date")
+    dates = utils.get_available_dates(mock_spark, table)
+
+    assert len(dates) == 3
+    assert dates == ["2023-01-01", "2023-01-03", "2023-01-02"]
+
+
+def test_get_available_dates_with_single_filter(spark, mocker):
+    mock_partition_df = spark.createDataFrame(
+        [
+            ("REGION_A", "2023-01-01"),
+            ("REGION_A", "2023-01-02"),
+            ("REGION_B", "2023-01-01"),
+            ("REGION_B", "2023-01-02"),
+        ],
+        schema="region string, info_date string",
+    )
+
+    mock_spark = mocker.MagicMock()
+    mock_spark.sql.return_value = mock_partition_df
+
+    table = Table(catalog="cat", schema="sch", table="tab", partitions=["region", "info_date"], date_column="info_date")
+    dates = utils.get_available_dates(mock_spark, table, filters={"region": "REGION_A"})
+
+    assert len(dates) == 2
+    assert dates == ["2023-01-01", "2023-01-02"]
+
+
+def test_get_available_dates_with_multiple_filters(spark, mocker):
+    mock_partition_df = spark.createDataFrame(
+        [
+            ("REGION_A", "TYPE_X", "2023-01-01"),
+            ("REGION_A", "TYPE_X", "2023-01-02"),
+            ("REGION_A", "TYPE_Y", "2023-01-01"),
+            ("REGION_B", "TYPE_X", "2023-01-01"),
+        ],
+        schema="region string, type string, info_date string",
+    )
+
+    mock_spark = mocker.MagicMock()
+    mock_spark.sql.return_value = mock_partition_df
+
+    table = Table(
+        catalog="cat", schema="sch", table="tab", partitions=["region", "type", "info_date"], date_column="info_date"
+    )
+    dates = utils.get_available_dates(mock_spark, table, filters={"region": "REGION_A", "type": "TYPE_X"})
+
+    assert len(dates) == 2
+    assert dates == ["2023-01-01", "2023-01-02"]
+
+
+def test_get_available_dates_missing_date_column(spark, mocker):
+    mock_partition_df = spark.createDataFrame(
+        [
+            ("2023-01-01",),
+        ],
+        schema="info_date string",
+    )
+
+    mock_spark = mocker.MagicMock()
+    mock_spark.sql.return_value = mock_partition_df
+
+    table = Table(catalog="cat", schema="sch", table="tab", partitions="info_date", date_column="wrong_column")
+
+    with pytest.raises(ValueError, match="date_column 'wrong_column' not found"):
+        utils.get_available_dates(mock_spark, table)
+
+
+def test_get_available_dates_missing_filter_column(spark, mocker):
+    mock_partition_df = spark.createDataFrame(
+        [
+            ("REGION_A", "2023-01-01"),
+        ],
+        schema="region string, info_date string",
+    )
+
+    mock_spark = mocker.MagicMock()
+    mock_spark.sql.return_value = mock_partition_df
+
+    table = Table(catalog="cat", schema="sch", table="tab", partitions=["region", "info_date"])
+
+    with pytest.raises(ValueError, match="Filter column 'wrong_filter' not found"):
+        utils.get_available_dates(mock_spark, table, filters={"wrong_filter": "value"})
+
+
+def test_get_rows_from_history_with_output_rows(spark, mocker):
+    from pyspark.sql import Row
+
+    mock_history_df = spark.createDataFrame(
+        [
+            Row(version=5, operationMetrics={"numOutputRows": "1234"}),
+            Row(version=4, operationMetrics={"numOutputRows": "500"}),
+        ]
+    )
+
+    mock_spark = mocker.MagicMock()
+    mock_spark.sql.return_value = mock_history_df
+
+    rows = utils.get_rows_from_history(mock_spark, "catalog.schema.table", "5")
+
+    assert rows == 1234
+    mock_spark.sql.assert_called_once_with("DESCRIBE HISTORY catalog.schema.table")
+
+
+def test_get_rows_from_history_no_output_rows(spark, mocker):
+    from pyspark.sql import Row
+
+    mock_history_df = spark.createDataFrame(
+        [Row(version=5, operationMetrics={"numFiles": "10"})],
+        schema="version int, operationMetrics map<string,string>",
+    )
+
+    mock_spark = mocker.MagicMock()
+    mock_spark.sql.return_value = mock_history_df
+
+    rows = utils.get_rows_from_history(mock_spark, "catalog.schema.table", "5")
+
+    assert rows == 0
+
+
+def test_get_rows_from_history_version_not_found(spark, mocker):
+    from pyspark.sql import Row
+
+    mock_history_df = spark.createDataFrame([Row(version=4, operationMetrics={"numOutputRows": "500"})])
+
+    mock_spark = mocker.MagicMock()
+    mock_spark.sql.return_value = mock_history_df
+
+    rows = utils.get_rows_from_history(mock_spark, "catalog.schema.table", "5")
+
+    assert rows == 0
diff --git a/uv.lock b/uv.lock
index 5a6f718..ca84722 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,4 @@
 version = 1
-revision = 3
 requires-python = ">=3.11, <4.0"
 resolution-markers = [
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