viirya commented on code in PR #479:
URL: https://github.com/apache/arrow-site/pull/479#discussion_r1504598859
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_posts/2024-02-27-comet-donation.md:
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+---
+layout: post
+title: "Announcing Apache Arrow DataFusion Comet"
+date: "2024-02-27 00:00:00"
+author: pmc
+categories: [release]
+---
+<!--
+{% comment %}
+Licensed to the Apache Software Foundation (ASF) under one or more
+contributor license agreements. See the NOTICE file distributed with
+this work for additional information regarding copyright ownership.
+The ASF licenses this file to you under the Apache License, Version 2.0
+(the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+{% endcomment %}
+-->
+
+# Introduction
+The Apache Arrow PMC is pleased to announce the donation of the [Comet
project],
+a native Spark Accelerator built on [Apache Arrow DataFusion].
+
+Comet is a Apache Spark Accelerator that uses Apache Arrow DataFusion to
+accelerate Spark workloads. It is written in Rust and designed as a drop-in
+replacement for Spark's JVM based execution engine and offers significant
+performance improvements for some workloads as shown below.
+
+```text
+┌───────────────────────────┐
+│ Spark │
+│ Frontend / Optimizer │
+│ (JVM Based) │
+└───────────────────────────┘
+ │
+ ▼
+┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
+┃ Comet ┃
+┃ Execution Engine ┃
+┃ (Native Code) ┃
+┗━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
+```
+
+**Figure 1**: With Comet, users interact with the same Spark ecosystem and
tools
+such as Spark SQL. However, the jobs are executed by Comet, which significantly
Review Comment:
```suggestion
**Figure 1**: With Comet, users interact with the same Spark ecosystem,
tools and APIs
such as Spark SQL. Users' SQL queries still run through the mature and
advantaged query optimizer and planner of Spark SQL.
However, once the optimized and planned queries are ready to execute, the
execution jobs are delegated to Comet, which significantly
```
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