# data transformation

Data transformation is the process of changing data's values, structure, or format to meet the requirements of destination schemas or subsequent analysis.

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## Using Polars in unison with Databricks Unity Catalog

DevFeed: [Using Polars in unison with Databricks Unity Catalog](<https://devfeed.tech/articles/using-polars-in-unison-with-databricks-unity-catalog-28613.md>)

Original publisher: [Read original article](<https://www.marvelousmlops.io/p/using-polars-in-unison-with-databricks>)

Author: Maria Vechtomova

Published: 2025-05-24T13:57:40Z

Content type: tutorial

Language: en

Sources: [MarvelousMLOps](<https://devfeed.tech/sources/marvelousmlops.md>)

Topics: [databricks](<https://devfeed.tech/topics/databricks.md>), [data transformation](<https://devfeed.tech/topics/data-transformation.md>), [Apache Spark](<https://devfeed.tech/topics/spark.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>)

Tags: [benchmark](<https://devfeed.tech/tags/benchmark.md>), [data-transformation](<https://devfeed.tech/tags/data-transformation.md>), [databricks](<https://devfeed.tech/tags/databricks.md>), [performance](<https://devfeed.tech/tags/performance.md>), [spark](<https://devfeed.tech/tags/spark.md>)

### AI overview

This tutorial explains how to read data from Databricks Unity Catalog directly into Polars dataframes using PyArrow and Deltalake, avoiding an intermediate PySpark dataframe. It also presents a TPC-H benchmark comparing Polars with PySpark or Spark SQL for some data-transformation workloads, with reported potential performance improvements and cost savings for certain data sizes.

### Source excerpt

The advent of Polars is not surprising given the performance it delivers.