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The following commit(s) were added to refs/heads/master by this push:
     new b97460c  Updated README with Pinot UI GIF. (#6164)
b97460c is described below

commit b97460cc14b18d0ada4f4a3126c667c0d10dcb4c
Author: Kenny Bastani <k...@socialmoon.com>
AuthorDate: Mon Oct 26 15:13:32 2020 -0400

    Updated README with Pinot UI GIF. (#6164)
---
 README.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/README.md b/README.md
index 1ab6e42..bc3ba7c 100644
--- a/README.md
+++ b/README.md
@@ -66,6 +66,8 @@ Pinot was originally built at LinkedIn to power rich 
interactive real-time analy
 
 * **Cloud-native on Kubernetes**: Helm chart provides a horizontally scalable 
and fault-tolerant clustered deployment that is easy to manage using Kubernetes.
 
+<a href="https://docs.pinot.apache.org/basics/getting-started";><img 
src="https://gblobscdn.gitbook.com/assets%2F-LtH6nl58DdnZnelPdTc%2F-MK7-D-ozvqsINCqoisu%2F-MK7-IjsO2Nv5xufkHSj%2Fapache-pinot-ui.gif?alt=media&token=e8a58e78-4d6d-4dde-b83a-a55c84bb48bf";
 align="center" alt="Apache Pinot query console"/></a>
+
 ## When should I use Pinot?
 
 Pinot is designed to execute real-time OLAP queries with low latency on 
massive amounts of data and events. In addition to real-time stream ingestion, 
Pinot also supports batch use cases with the same low latency guarantees. It is 
suited in contexts where fast analytics, such as aggregations, are needed on 
immutable data, possibly, with real-time data ingestion. Pinot works very well 
for querying time series data with lots of dimensions and metrics.


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