diff --git a/datafusion-cli/README.md b/datafusion-cli/README.md
index 8f6856cb3358..1d99cfbcb00a 100644
--- a/datafusion-cli/README.md
+++ b/datafusion-cli/README.md
@@ -17,98 +17,12 @@
   under the License.
 -->
 
-# DataFusion Command-line Interface
-
-[DataFusion](https://github.com/apache/arrow-datafusion) is an extensible query execution framework, written in Rust, that uses Apache Arrow as its in-memory format.
-
-The DataFusion CLI allows SQL queries to be executed by an in-process DataFusion context.
-
-```ignore
-USAGE:
-    datafusion-cli [OPTIONS]
-
-OPTIONS:
-    -c, --batch-size <BATCH_SIZE>    The batch size of each query, or use DataFusion default
-    -f, --file <FILE>...             Execute commands from file(s), then exit
-        --format <FORMAT>            [default: table] [possible values: csv, tsv, table, json,
-                                     nd-json]
-    -h, --help                       Print help information
-    -p, --data-path <DATA_PATH>      Path to your data, default to current directory
-    -q, --quiet                      Reduce printing other than the results and work quietly
-    -r, --rc <RC>...                 Run the provided files on startup instead of ~/.datafusionrc
-    -V, --version                    Print version information
-
-```
-
-## Example
-
-Create a CSV file to query.
-
-```bash,ignore
-$ echo "1,2" > data.csv
-```
-
-```sql,ignore
-$ datafusion-cli
-
-DataFusion CLI v12.0.0
-
-> CREATE EXTERNAL TABLE foo (a INT, b INT) STORED AS CSV LOCATION 'data.csv';
-0 rows in set. Query took 0.001 seconds.
+<!-- Note this file is included in the crates.io page as well https://crates.io/crates/datafusion-cli -->
 
-> SELECT * FROM foo;
-+---+---+
-| a | b |
-+---+---+
-| 1 | 2 |
-+---+---+
-1 row in set. Query took 0.017 seconds.
-```
-
-## Querying S3 Data Sources
-
-The CLI can query data in S3 if the following environment variables are defined:
-
-- `AWS_REGION`
-- `AWS_ACCESS_KEY_ID`
-- `AWS_SECRET_ACCESS_KEY`
-
-Alternatively, you can supply a profile name via the `AWS_PROFILE` environment variable. When using a [named profile](https://docs.aws.amazon.com/cli/latest/userguide/cli-configure-files.html), the CLI obtains credentials from the profile configuration and thus does not require `AWS_ACCESS_KEY_ID` or `AWS_SECRET_ACCESS_KEY` environment variables to be set.
-
-Note that the region must be set to the region where the bucket exists until the following issue is resolved:
-
-- https://github.com/apache/arrow-rs/issues/2795
-
-Example:
-
-```bash
-$ aws s3 cp test.csv s3://my-bucket/
-upload: ./test.csv to s3://my-bucket/test.csv
-
-$ export AWS_REGION=us-east-1
-$ export AWS_SECRET_ACCESS_KEY=***************************
-$ export AWS_ACCESS_KEY_ID=**************
-
-$ ./target/release/datafusion-cli
-DataFusion CLI v12.0.0
-❯ create external table test stored as csv location 's3://my-bucket/test.csv';
-0 rows in set. Query took 0.374 seconds.
-❯ select * from test;
-+----------+----------+
-| column_1 | column_2 |
-+----------+----------+
-| 1        | 2        |
-+----------+----------+
-1 row in set. Query took 0.171 seconds.
-```
-
-## DataFusion-Cli
+# DataFusion Command-line Interface
 
-Build the `datafusion-cli` by `cd` into the sub-directory:
+[DataFusion](https://arrow.apache.org/datafusion/) is an extensible query execution framework, written in Rust, that uses Apache Arrow as its in-memory format.
 
-```bash
-cd datafusion-cli
-cargo build
-```
+The DataFusion CLI is a command line utility that runs SQL queries using the DataFusion engine.
 
-[df]: https://crates.io/crates/datafusion
+See the [`datafusion-cli` documentation](https://arrow.apache.org/datafusion/user-guide/cli.html) for further information.
diff --git a/docs/source/user-guide/cli.md b/docs/source/user-guide/cli.md
index beac0cc44234..5954f0aaebd7 100644
--- a/docs/source/user-guide/cli.md
+++ b/docs/source/user-guide/cli.md
@@ -21,7 +21,7 @@
 
 The DataFusion CLI is a command-line interactive SQL utility for executing
 queries against any supported data files. It is a convenient way to
-try DataFusion out with your own data sources, and test out its SQL support.
+try DataFusion's SQL support with your own data.
 
 ## Example