doc: add few use cases (#318)

Signed-off-by: Vigith Maurice <[email protected]>
numaproj · Nov 5, 2022 · 2355978 · 2355978
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diff --git a/README.md b/README.md
@@ -9,9 +9,18 @@
 
 ## Summary
 
-Numaflow is a Kubernetes-native tool for running massively parallel stream processing. A Numaflow Pipeline is implemented as a Kubernetes custom resource and consists of one or more source, data processing, and sink vertices.
+Numaflow is a Kubernetes-native tool for running massively parallel stream processing. A Numaflow Pipeline is implemented 
+as a Kubernetes custom resource and consists of one or more source, data processing, and sink vertices.
 
-Numaflow installs in a few minutes and is easier and cheaper to use for simple data processing applications than a full-featured stream processing platforms.
+Numaflow installs in a few minutes and is easier and cheaper to use for simple data processing applications than a 
+full-featured stream processing platforms.
+
+## Use Cases
+
+- Real-time data analytics applications. 
+- Event driven applications such as anomaly detection, monitoring and alerting. 
+- Streaming applications such as data instrumentation and data movement. 
+- Workflows running in a streaming manner.
 
 ## Key Features
 

diff --git a/docs/README.md b/docs/README.md
@@ -1,8 +1,17 @@
 # Numaflow
 
-Numaflow is a Kubernetes-native tool for running massively parallel stream processing. A Numaflow Pipeline is implemented as a Kubernetes custom resource and consists of one or more source, data processing, and sink vertices.
+Numaflow is a Kubernetes-native tool for running massively parallel stream processing. A Numaflow Pipeline is implemented 
+as a Kubernetes custom resource and consists of one or more source, data processing, and sink vertices.
 
-Numaflow installs in a few minutes and is easier and cheaper to use for simple data processing applications than a full-featured stream processing platforms.
+Numaflow installs in a few minutes and is easier and cheaper to use for simple data processing applications than a full-featured 
+stream processing platforms.
+
+## Use Cases
+
+- Real-time data analytics applications.
+- Event driven applications such as anomaly detection, monitoring and alerting.
+- Streaming applications such as data instrumentation and data movement.
+- Workflows running in a streaming manner.
 
 ## Key Features