docs: lead README with the Arrow-native framing (#4428)

Author: andygrove

README.md

@@ -35,10 +35,12 @@ under the License.
 
 <img src="docs/source/_static/images/DataFusionComet-Logo-Light.png" width="512" alt="logo"/>
 
-Apache DataFusion Comet is a high-performance accelerator for Apache Spark, built on top of the powerful
-[Apache DataFusion] query engine. Comet is designed to significantly enhance the
-performance of Apache Spark workloads while leveraging commodity hardware and seamlessly integrating with the
-Spark ecosystem without requiring any code changes.
+Apache DataFusion Comet is a high-performance accelerator for Apache Spark. Comet keeps Spark queries
+**Arrow-native end-to-end**: operators, expressions, shuffle, and broadcast all stay in Apache Arrow
+columnar format, avoiding the per-row overhead of Spark's row-based engine. Within the Arrow-native
+pipeline, operators and expressions execute as Rust code (via the [Apache DataFusion] query engine)
+or as JVM code that operates directly on Arrow batches. Comet integrates with the Spark ecosystem
+without requiring any code changes.
 
 **Comet provides a ~2x speedup for TPC-DS @ SF 1000 (1TB), resulting in ~50% cost savings.**
 
@@ -58,17 +60,20 @@ See the [Comet Benchmarking Guide](https://datafusion.apache.org/comet/contribut
 
 ## What Comet Accelerates
 
-Comet replaces Spark operators and expressions with native Rust implementations that run on Apache DataFusion.
-It uses Apache Arrow for zero-copy data transfer between the JVM and native code.
+Comet accelerates Spark workloads by replacing Spark operators and expressions with high-performance implementations that process Apache Arrow columnar data directly. Most operators are powered by native Rust execution built on Apache DataFusion, while others run efficiently in the JVM on Arrow batches. This unified columnar execution model keeps processing within the Comet engine end-to-end, reducing overhead and delivering faster, more efficient query execution without reverting to Spark's traditional row-based engine.
 
 - **Parquet scans**: native Parquet reader integrated with Spark's query planner
 - **Apache Iceberg**: accelerated Parquet scans when reading Iceberg tables from Spark
   (see the [Iceberg guide](https://datafusion.apache.org/comet/user-guide/iceberg.html))
-- **Shuffle**: native columnar shuffle with support for hash and range partitioning
+- **Shuffle**: Arrow-IPC columnar shuffle with support for hash and range partitioning, in a native Rust
+  implementation paired with a JVM fallback for unsupported partition key types
 - **Expressions**: hundreds of supported Spark expressions across math, string, datetime, array,
   map, JSON, hash, and predicate categories
 - **Aggregations**: hash aggregate with support for `FILTER (WHERE ...)` clauses
 - **Joins**: hash join, sort-merge join, and broadcast join
+- **Scala/Java UDFs**: support for keeping Scala/Java scalar UDFs in the Comet pipeline
+  via Spark's whole-stage codegen (see the
+  [Scala UDF guide](https://datafusion.apache.org/comet/user-guide/scala_java_udfs.html))
 
 For the authoritative lists, see the [supported expressions](https://datafusion.apache.org/comet/user-guide/expressions.html)
 and [supported operators](https://datafusion.apache.org/comet/user-guide/operators.html) pages.