[beam] branch master updated: BEAM-10095: Add Runner and SDK links to Beam overview page

[ibzib]

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ibzib pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/beam.git


The following commit(s) were added to refs/heads/master by this push:
     new 5a360a8  BEAM-10095: Add Runner and SDK links to Beam overview page
     new cdc7226  Merge pull request #13774 from pcoet/BEAM-10095
5a360a8 is described below

commit 5a360a86d74f40af4d92dc6297190d5847acdaa8
Author: David Huntsperger <dhuntsper...@google.com>
AuthorDate: Tue Jan 19 19:00:35 2021 +0000

    BEAM-10095: Add Runner and SDK links to Beam overview page
---
 .../site/content/en/get-started/beam-overview.md   | 24 ++++++++++++----------
 1 file changed, 13 insertions(+), 11 deletions(-)

diff --git a/website/www/site/content/en/get-started/beam-overview.md 
b/website/www/site/content/en/get-started/beam-overview.md
index 73c05eb..40aacb0 100644
--- a/website/www/site/content/en/get-started/beam-overview.md
+++ b/website/www/site/content/en/get-started/beam-overview.md
@@ -23,7 +23,7 @@ limitations under the License.
 
 Apache Beam is an open source, unified model for defining both batch and 
streaming data-parallel processing pipelines. Using one of the open source Beam 
SDKs, you build a program that defines the pipeline. The pipeline is then 
executed by one of Beam's supported **distributed processing back-ends**, which 
include [Apache Flink](https://flink.apache.org), [Apache 
Spark](http://spark.apache.org), and [Google Cloud 
Dataflow](https://cloud.google.com/dataflow).
 
-Beam is particularly useful for [Embarrassingly 
Parallel](https://en.wikipedia.org/wiki/Embarassingly_parallel) data processing 
tasks, in which the problem can be decomposed into many smaller bundles of data 
that can be processed independently and in parallel. You can also use Beam for 
Extract, Transform, and Load (ETL) tasks and pure data integration. These tasks 
are useful for moving data between different storage media and data sources, 
transforming data into a more desirable format,  [...]
+Beam is particularly useful for [embarrassingly 
parallel](https://en.wikipedia.org/wiki/Embarassingly_parallel) data processing 
tasks, in which the problem can be decomposed into many smaller bundles of data 
that can be processed independently and in parallel. You can also use Beam for 
Extract, Transform, and Load (ETL) tasks and pure data integration. These tasks 
are useful for moving data between different storage media and data sources, 
transforming data into a more desirable format,  [...]
 
 ## Apache Beam SDKs
 
@@ -31,9 +31,9 @@ The Beam SDKs provide a unified programming model that can 
represent and transfo
 
 Beam currently supports the following language-specific SDKs:
 
-- Java ![Java logo](/images/logos/sdks/java.png)
-- Python ![Python logo](/images/logos/sdks/python.png)
-- Go <img src="/images/logos/sdks/go.png" height="45px" alt="Go logo">
+- [Apache Beam Java SDK](/documentation/sdks/java) ![Java 
logo](/images/logos/sdks/java.png)
+- [Apache Beam Python SDK](/documentation/sdks/python) ![Python 
logo](/images/logos/sdks/python.png)
+- [Apache Beam Go SDK](/documentation/sdks/go) <img 
src="/images/logos/sdks/go.png" height="45px" alt="Go logo">
 
 A Scala <img src="/images/logos/sdks/scala.png" height="45px" alt="Scala 
logo"> interface is also available as [Scio](https://github.com/spotify/scio).
 
@@ -41,14 +41,16 @@ A Scala <img src="/images/logos/sdks/scala.png" 
height="45px" alt="Scala logo">
 
 The Beam Pipeline Runners translate the data processing pipeline you define 
with your Beam program into the API compatible with the distributed processing 
back-end of your choice. When you run your Beam program, you'll need to specify 
an [appropriate runner](/documentation/runners/capability-matrix) for the 
back-end where you want to execute your pipeline.
 
-Beam currently supports Runners that work with the following distributed 
processing back-ends:
+Beam currently supports the following runners:
 
-- Apache Flink ![Apache Flink logo](/images/logos/runners/flink.png)
-- Apache Samza <img src="/images/logos/runners/samza.png" height="20px" 
alt="Apache Samza logo">
-- Apache Spark ![Apache Spark logo](/images/logos/runners/spark.png)
-- Google Cloud Dataflow ![Google Cloud Dataflow 
logo](/images/logos/runners/dataflow.png)
-- Hazelcast Jet ![Hazelcast Jet logo](/images/logos/runners/jet.png)
-- Twister2 ![Twister2 logo](/images/logos/runners/twister2.png)
+- [Direct Runner](/documentation/runners/direct)
+- [Apache Flink Runner](/documentation/runners/flink) ![Apache Flink 
logo](/images/logos/runners/flink.png)
+- [Apache Nemo Runner](/documentation/runners/nemo)
+- [Apache Samza Runner](/documentation/runners/samza) <img 
src="/images/logos/runners/samza.png" height="20px" alt="Apache Samza logo">
+- [Apache Spark Runner](/documentation/runners/spark) ![Apache Spark 
logo](/images/logos/runners/spark.png)
+- [Google Cloud Dataflow Runner](/documentation/runners/dataflow) ![Google 
Cloud Dataflow logo](/images/logos/runners/dataflow.png)
+- [Hazelcast Jet Runner](/documentation/runners/jet) ![Hazelcast Jet 
logo](/images/logos/runners/jet.png)
+- [Twister2 Runner](/documentation/runners/twister2) ![Twister2 
logo](/images/logos/runners/twister2.png)
 
 **Note:** You can always execute your pipeline locally for testing and 
debugging purposes.