docs: Correct Arrow misconception (#21053)

docs/source/index.md

@@ -30,10 +30,12 @@ in Rust, and available for Python, R and NodeJS.
   data to be in memory at the same time.
 - **Parallel**: Utilises the power of your machine by dividing the workload among the available CPU
   cores without any additional configuration.
-- **Vectorized Query Engine**: Using [Apache Arrow](https://arrow.apache.org/), a columnar data
-  format, to process your queries in a vectorized manner and SIMD to optimize CPU usage.
+- **Vectorized Query Engine**
 - **GPU Support**: Optionally run queries on NVIDIA GPUs for maximum performance for in-memory
   workloads.
+- **[Apache Arrow support](https://arrow.apache.org/)**: Polars consume and produce Arrow data often
+  zero-copy. Note that Polars is not build on a Pyarrow/Arrow implementation. Polars has written all
+  compute and buffer implementations.
 
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