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:

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.

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"])