nealrichardson commented on a change in pull request #63:
URL: https://github.com/apache/arrow-site/pull/63#discussion_r449257551



##########
File path: index.html
##########
@@ -1,72 +1,62 @@
 ---
-layout: default
+layout: home
 ---
-<div class="jumbotron">
-    <h1>Apache Arrow</h1>
-    <p class="lead">A cross-language development platform for in-memory 
data</p>
-    <p>
-        <a class="btn btn-lg btn-success" style="white-space: normal;" 
href="mailto:[email protected]"; role="button">Join Mailing List</a>
-        <a class="btn btn-lg btn-primary" style="white-space: normal;" 
href="{{ site.baseurl }}/install/" role="button">Install 
({{site.data.versions['current'].number}} Release - 
{{site.data.versions['current'].date}})</a>
-    </p>
-</div>
-<h5>
-  Interested in contributing?
-  <small class="text-muted">Join the <a 
href="http://mail-archives.apache.org/mod_mbox/arrow-dev/";><strong>mailing 
list</strong></a> or check out the <a 
href="https://cwiki.apache.org/confluence/display/ARROW";><strong>developer 
wiki</strong></a>.</small>
-</h5>
-<h5>
-  <a href="{{ site.baseurl }}/blog/"><strong>See Latest News</strong></a>
-</h5>
-<p>
-  {{ site.description }}
-</p>
-<hr />
+<h1>What is Arrow?</h1>
 <div class="row">
   <div class="col-lg-4">
-      <h2 class="mt-3">Fast</h2>
-      <p>Apache Arrow&#8482; enables execution engines to take advantage of 
the latest SIMD (Single instruction, multiple data) operations included in 
modern processors, for native vectorized optimization of analytical data 
processing. Columnar layout is optimized for data locality for better 
performance on modern hardware like CPUs and GPUs.</p>
-      <p>The Arrow memory format supports <strong>zero-copy reads</strong> for 
lightning-fast data access without serialization overhead.</p>
+      <h2 class="mt-3">Format</h2>
+      <p>Apache Arrow defines a language-independent columnar memory format 
for flat and hierarchical data, organized for efficient analytic operations on 
modern hardware like CPUs and GPUs. The Arrow memory format also supports 
<strong>zero-copy reads</strong> for lightning-fast data access without 
serialization overhead.</p>
+      <p><a href="{{ site.baseurl }}/overview/">Learn more</a> about the 
design or
+        <a href="{{ site.baseurl }}/docs/format/Columnar.html">read the 
specification</a>.</p>
   </div>
   <div class="col-lg-4">
-      <h2 class="mt-3">Flexible</h2>
-      <p>Arrow acts as a new high-performance interface between various 
systems. It is also focused on supporting a wide variety of industry-standard 
programming languages. C, C++, C#, Go, Java, JavaScript, MATLAB, Python, R, 
Ruby, and Rust implementations are in progress and more languages are welcome.
+      <h2 class="mt-3">Libraries</h2>
+      <p>The Arrow project includes libraries that implement the memory 
specification in many languages. They enable you to use the Arrow format as an 
efficient means of sharing data across languages and processes. Libraries are 
available for <a href="{{ site.baseurl }}/docs/c_glib/">C</a>, <a href="{{ 
site.baseurl }}/docs/cpp/">C++</a>, <a 
href="https://github.com/apache/arrow/blob/master/csharp/README.md";>C#</a>, <a 
href="https://godoc.org/github.com/apache/arrow/go/arrow";>Go</a>, <a href="{{ 
site.baseurl }}/docs/java/">Java</a>, <a href="{{ site.baseurl 
}}/docs/js/">JavaScript</a>, <a 
href="https://github.com/apache/arrow/blob/master/matlab/README.md";>MATLAB</a>, 
<a href="{{ site.baseurl }}/docs/python/">Python</a>, <a href="{{ site.baseurl 
}}/docs/r/">R</a>, <a 
href="https://github.com/apache/arrow/blob/master/ruby/README.md";>Ruby</a>, and 
<a href="https://docs.rs/crate/arrow/";>Rust</a>.
       </p>
+      See <a href="{{ site.baseurl }}/install/">how to install</a> and <a 
href="{{ site.baseurl }}/getting_started/">get started</a>.
   </div>
   <div class="col-lg-4">
-      <h2 class="mt-3">Standard</h2>
-      <p>Apache Arrow is backed by key developers of 13 major open source 
projects, including Calcite, Cassandra, Drill, Hadoop, HBase, Ibis, Impala, 
Kudu, Pandas, Parquet, Phoenix, Spark, and Storm making it the de-facto 
standard for columnar in-memory analytics.</p>
-      <p>Learn more about projects that are <a href="{{ site.baseurl 
}}/powered_by/">Powered By Apache Arrow</a></p>
+      <h2 class="mt-3">Applications</h2>
+      <p>Arrow libraries provide a foundation for developers to build fast 
analytics applications. <a href="{{ site.baseurl }}/powered_by/">Many popular 
projects</a> use Arrow to ship columnar data efficiently or as the basis for 
analytic engines.
+      <p>The libraries also include built-in features for working with data 
directly, including Parquet file reading and querying large datasets. See more 
Arrow <a href="{{ site.baseurl }}/use_cases/">use cases</a>.</p>

Review comment:
       I did this and I think it works well. Will try to fill in the Overview 
page now, as best I can.




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