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doc: add few use cases #318

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13 changes: 11 additions & 2 deletions README.md
Original file line number Diff line number Diff line change
Expand Up @@ -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

Expand Down
13 changes: 11 additions & 2 deletions docs/README.md
Original file line number Diff line number Diff line change
@@ -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

Expand Down