# Advanced Data Processing with Koheesio

In this guide, we will explore some advanced data processing techniques using Koheesio. We will cover topics such as 
complex transformations, handling large datasets, and optimizing performance.

## Complex Transformations

Koheesio provides a variety of built-in transformations, but sometimes you may need to perform more complex operations 
on your data. In such cases, you can create custom transformations.

Here's an example of a custom transformation that normalizes a column in a DataFrame:

```python
from pyspark.sql import DataFrame
from koheesio.spark.transformations.transform 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

    def transform(self, df: DataFrame) -> DataFrame:
        return normalize_column(df, self.column)
```

## Handling Large Datasets
When working with large datasets, it's important to manage resources effectively to ensure good performance. Koheesio 
provides several features to help with this.  

## Partitioning
Partitioning is a technique that divides your data into smaller, more manageable pieces, called partitions. Koheesio 
allows you to specify the partitioning scheme for your data when writing it to a target.

```python
from koheesio.spark.writers.delta import DeltaTableWriter
from koheesio.spark.etl_task import EtlTask

class MyTask(EtlTask):
    target = DeltaTableWriter(table="my_table", partitionBy=["column1", "column2"])
```

[//]: # (## Caching)

[//]: # (Caching is another technique that can improve performance by storing the result of a transformation in memory, so it )

[//]: # (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.spark.transformations.cache import CacheTransformation)

[//]: # ()
[//]: # (class MyTask&#40;EtlTask&#41;:)

[//]: # (    transformations = [NormalizeColumnTransform&#40;column="my_column"&#41;, CacheTransformation&#40;&#41;])

[//]: # (```)

[//]: # ()
