andygrove commented on code in PR #17:
URL: https://github.com/apache/datafusion-site/pull/17#discussion_r1848493281
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_posts/2024-11-19-datafusion-python-udf-comparisons.md:
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+---
+layout: post
+title: "Comparing approaches to User Defined Functions in Apache DataFusion
using Python"
+date: "2024-11-19 00:00:00"
+author: timsaucer
+categories: [tutorial]
+---
+
+<!--
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+# Writing User Defined Functions in Apache DataFusion using Python
+
+## Personal Context
+
+For a few months now I’ve been working with [Apache
DataFusion](https://datafusion.apache.org/), a
+fast query engine written in Rust. From my experience the language that nearly
all data scientists
+are working in is Python. In general, often stick to
[Pandas](https://pandas.pydata.org/) for
+in-memory tasks and [PySpark](https://spark.apache.org/) for larger tasks that
require distributed
+processing.
+
+In addition to DataFusion, there is another Rust based newcomer to the
DataFrame world,
+[Polars](https://pola.rs/). It is growing extremely fast, and it serves many
of the same use cases
+as DataFusion. For my use cases, I'm interested in DataFusion because I want
to be able to build
+small scale tests rapidly and then scale them up to larger distributed systems
with ease. I do
+recommend evaluating Polars for in memory work.
Review Comment:
```suggestion
recommend evaluating Polars for in-memory work.
```
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