AirFlow integration link (#6077)

* Update README.rst
* typo fix

README.rst

@@ -182,6 +182,12 @@ Fedora / CentOS (rpm)
 Comparison to related technologies
 ==================================
 
+#. *Data Engineering* tools such as `AirFlow <https://airflow.apache.org/>`_, `Luigi <https://github.com/spotify/luigi>`_ and
+   others - in DVC data, model and ML pipelines represent a single ML project and focuses on data scientist' experience while
+   data engineering pipeline orchestrates multiple data projects and focuses on efficent execution. DVC project can be used
+   from data pipeline as a single execution step. `AirFlow DVC <https://github.com/covid-genomics/airflow-dvc>`_ is an example
+   of such integration.
+
 #. `Git-annex <https://git-annex.branchable.com/>`_ - DVC uses the idea of storing the content of large files (which should
    not be in a Git repository) in a local key-value store, and uses file hardlinks/symlinks instead of
    copying/duplicating files.
@@ -195,7 +201,8 @@ Comparison to related technologies
 #. `Workflow Management Systems <https://en.wikipedia.org/wiki/Workflow_management_system>`_ - DVC is a workflow
    management system designed specifically to manage machine learning experiments. DVC is built on top of Git.
 
-#. `DAGsHub <https://dagshub.com/>`_ - This is a Github equivalent for DVC. Pushing Git+DVC based repositories to DAGsHub will produce in a high level project dashboard; including DVC pipelines and metrics visualizations, as well as links to any DVC-managed files present in cloud storage.
+#. `DAGsHub <https://dagshub.com/>`_ - online service of Git+DVC repositories with pipeline and metrics visualization and
+   DVC-specific cloud storage.
 
 Contributing
 ============