Feat/6 restructure module structure

docs/reference/concepts/transformations.md

@@ -161,13 +161,13 @@ Koheesio provides a variety of `Transformation` subclasses for transforming data
 examples:
 
 - `DataframeLookup`: This transformation joins two dataframes together based on a list of join mappings. It allows you
-    to specify the join type and join hint, and it supports selecting specific target columns from the right dataframe.
+  to specify the join type and join hint, and it supports selecting specific target columns from the right dataframe.
 
     Here's an example of how to use the `DataframeLookup` transformation:
 
     ```python
     from pyspark.sql import SparkSession
-    from koheesio.steps.transformations.lookup import DataframeLookup, JoinMapping, TargetColumn, JoinType
+    from koheesio.steps.transformations import DataframeLookup, JoinMapping, TargetColumn, JoinType
 
     spark = SparkSession.builder.getOrCreate()
     left_df = spark.createDataFrame([(1, "A"), (2, "B")], ["id", "value"])
@@ -246,7 +246,7 @@ how to chain transformations:
 ```python
 from pyspark.sql import SparkSession
 from koheesio.steps.transformations import HashUUID5
-from koheesio.steps.transformations.lookup import DataframeLookup, JoinMapping, TargetColumn, JoinType
+from koheesio.steps.transformations import DataframeLookup, JoinMapping, TargetColumn, JoinType
 
 # Create a SparkSession
 spark = SparkSession.builder.getOrCreate()

docs/tutorials/advanced-data-processing.md

@@ -10,13 +10,15 @@ Here's an example of a custom transformation that normalizes a column in a DataF
 
 ```python
 from pyspark.sql import DataFrame
-from koheesio.steps.transformations.transform import Transform
+from koheesio.steps.transformations import Transform
+
 
 def normalize_column(df: DataFrame, column: str) -> DataFrame:
     max_value = df.agg({column: "max"}).collect()[0][0]
     min_value = df.agg({column: "min"}).collect()[0][0]
     return df.withColumn(column, (df[column] - min_value) / (max_value - min_value))
 
+
 class NormalizeColumnTransform(Transform):
     column: str
 
@@ -45,7 +47,8 @@ Caching is another technique that can improve performance by storing the result
 doesn't have to be recomputed each time it's used. You can use the cache method to cache the result of a transformation.
 
 ```python
-from koheesio.steps.transformations.cache import CacheTransformation
+from koheesio.steps.transformations import CacheTransformation
+
 
 class MyTask(EtlTask):
     transformations = [NormalizeColumnTransform(column="my_column"), CacheTransformation()]

docs/tutorials/hello-world.md

@@ -52,12 +52,14 @@ This example demonstrates how to use the `EtlTask` from the `koheesio` library t
 ```python
 from typing import Any
 from pyspark.sql import DataFrame, functions as f
-from koheesio.steps.transformations.transform import Transform
+from koheesio.steps.transformations import Transform
 from koheesio.tasks.etl_task import EtlTask
 
+
 def add_column(df: DataFrame, target_column: str, value: Any):
     return df.withColumn(target_column, f.lit(value))
 
+
 class MyFavoriteMovieTask(EtlTask):
     my_favorite_movie: str
 
@@ -102,7 +104,7 @@ source:
 ```python
 from pyspark.sql import SparkSession
 from koheesio.context import Context
-from koheesio.steps.readers.dummy import DummyReader
+from koheesio.steps.readers import DummyReader
 from koheesio.steps.writers.dummy import DummyWriter
 
 context = Context.from_yaml("sample.yaml")

docs/tutorials/testing-koheesio-tasks.md

@@ -10,15 +10,17 @@ Here's an example of how to unit test a Koheesio task:
 
 ```python
 from koheesio.tasks.etl_task import EtlTask
-from koheesio.steps.readers.dummy import DummyReader
+from koheesio.steps.readers import DummyReader
 from koheesio.steps.writers.dummy import DummyWriter
-from koheesio.steps.transformations.transform import Transform
+from koheesio.steps.transformations import Transform
 from pyspark.sql import SparkSession, DataFrame
 from pyspark.sql.functions import col
 
+
 def filter_age(df: DataFrame) -> DataFrame:
     return df.filter(col("Age") > 18)
 
+
 def test_etl_task():
     # Initialize SparkSession
     spark = SparkSession.builder.getOrCreate()
@@ -61,15 +63,17 @@ Here's an example of how to write an integration test for this task:
 ```python
 # my_module.py
 from koheesio.tasks.etl_task import EtlTask
-from koheesio.steps.readers.delta import DeltaReader
+from koheesio.spark.readers.delta import DeltaReader
 from koheesio.steps.writers.delta import DeltaWriter
-from koheesio.steps.transformations.transform import Transform
+from koheesio.steps.transformations import Transform
 from koheesio.context import Context
 from pyspark.sql.functions import col
 
+
 def filter_age(df):
     return df.filter(col("Age") > 18)
 
+
 context = Context({
     "reader_options": {
         "table": "input_table"