Improve comments on target user and unify summaries (#12418)

README.md

@@ -41,9 +41,28 @@
   <img src="./docs/source/_static/images/2x_bgwhite_original.png" width="512" alt="logo"/>
 </a>
 
-Apache DataFusion is a very fast, extensible query engine for building high-quality data-centric systems in
-[Rust](http://rustlang.org), using the [Apache Arrow](https://arrow.apache.org)
-in-memory format. [Python Bindings](https://github.com/apache/datafusion-python) are also available. DataFusion offers SQL and Dataframe APIs, excellent [performance](https://benchmark.clickhouse.com/), built-in support for CSV, Parquet, JSON, and Avro, extensive customization, and a great community.
+DataFusion is an extensible query engine written in [Rust] that
+uses [Apache Arrow] as its in-memory format. DataFusion's target users are
+developers building fast and feature rich database and analytic systems,
+customized to particular workloads. See [use cases] for examples.
+
+"Out of the box," DataFusion offers [SQL] and [`Dataframe`] APIs,
+excellent [performance], built-in support for CSV, Parquet, JSON, and Avro,
+extensive customization, and a great community.
+[Python Bindings] are also available.
+
+DataFusion features a full query planner, a columnar, streaming, multi-threaded,
+vectorized execution engine, and partitioned data sources. You can
+customize DataFusion at almost all points including additional data sources,
+query languages, functions, custom operators and more.
+See the [Architecture] section for more details.
+
+[rust]: http://rustlang.org
+[apache arrow]: https://arrow.apache.org
+[use cases]: https://datafusion.apache.org/user-guide/introduction.html#use-cases
+[python bindings]: https://github.com/apache/datafusion-python
+[performance]: https://benchmark.clickhouse.com/
+[architecture]: https://datafusion.apache.org/contributor-guide/architecture.html
 
 Here are links to some important information
 

datafusion/core/src/lib.rs

@@ -17,24 +17,28 @@
 #![warn(missing_docs, clippy::needless_borrow)]
 
 //! [DataFusion] is an extensible query engine written in Rust that
-//! uses [Apache Arrow] as its in-memory format. DataFusion help developers
-//! build fast and feature rich database and analytic systems, customized to
-//! particular workloads. See [use cases] for examples
+//! uses [Apache Arrow] as its in-memory format. DataFusion's target users are
+//! developers building fast and feature rich database and analytic systems,
+//! customized to particular workloads. See [use cases] for examples.
 //!
-//! "Out of the box," DataFusion quickly runs complex [SQL] and
-//! [`DataFrame`] queries using a full-featured query planner, a columnar,
-//! streaming, multi-threaded, vectorized execution engine, and partitioned data
-//! sources (Parquet, CSV, JSON, and Avro).
+//! "Out of the box," DataFusion offers [SQL] and [`Dataframe`] APIs,
+//! excellent [performance], built-in support for CSV, Parquet, JSON, and Avro,
+//! extensive customization, and a great community.
+//! [Python Bindings] are also available.
 //!
-//! DataFusion is designed for easy customization such as
-//! additional data sources, query languages, functions, custom
-//! operators and more. See the [Architecture] section for more details.
+//! DataFusion features a full query planner, a columnar, streaming, multi-threaded,
+//! vectorized execution engine, and partitioned data  sources. You can
+//! customize DataFusion at almost all points including additional data sources,
+//! query languages, functions, custom operators and more.
+//! See the [Architecture] section below for more details.
 //!
 //! [DataFusion]: https://datafusion.apache.org/
 //! [Apache Arrow]: https://arrow.apache.org
 //! [use cases]: https://datafusion.apache.org/user-guide/introduction.html#use-cases
 //! [SQL]: https://datafusion.apache.org/user-guide/sql/index.html
 //! [`DataFrame`]: dataframe::DataFrame
+//! [performance]: https://benchmark.clickhouse.com/
+//! [Python Bindings]: https://github.com/apache/datafusion-python
 //! [Architecture]: #architecture
 //!
 //! # Examples

docs/source/index.rst

@@ -32,14 +32,23 @@ Apache DataFusion
      <a class="github-button" href="https://github.com/apache/datafusion/fork" data-size="large" data-show-count="true" aria-label="Fork apache/datafusion on GitHub">Fork</a>
   </p>
 
-DataFusion is a very fast, extensible query engine for building high-quality data-centric systems in
-`Rust <http://rustlang.org>`_, using the `Apache Arrow <https://arrow.apache.org>`_
-in-memory format.
-
-DataFusion offers SQL and Dataframe APIs, excellent
-`performance <https://benchmark.clickhouse.com>`_, built-in support for
-CSV, Parquet, JSON, and Avro, extensive customization, and a great
-community.
+
+DataFusion is an extensible query engine written in `Rust <http://rustlang.org>`_ that
+uses `Apache Arrow <https://arrow.apache.org>`_ as its in-memory format. DataFusion's target users are
+developers building fast and feature rich database and analytic systems,
+customized to particular workloads. See `use cases <https://datafusion.apache.org/user-guide/introduction.html#use-cases>`_ for examples.
+
+"Out of the box," DataFusion offers `SQL <https://datafusion.apache.org/user-guide/sql/index.html>`_
+and `Dataframe <https://docs.rs/datafusion/latest/datafusion/dataframe/struct.DataFrame.html>`_ APIs,
+excellent `performance <https://benchmark.clickhouse.com/>`_, built-in support for CSV, Parquet, JSON, and Avro,
+extensive customization, and a great community.
+`Python Bindings <https://github.com/apache/datafusion-python>`_ are also available.
+
+DataFusion features a full query planner, a columnar, streaming, multi-threaded,
+vectorized execution engine, and partitioned data sources. You can
+customize DataFusion at almost all points including additional data sources,
+query languages, functions, custom operators and more.
+See the `Architecture <https://datafusion.apache.org/contributor-guide/architecture.html>`_ section for more details.
 
 To get started, see