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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 <[email protected]>
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 
-- Python 
-- Go <img src="/images/logos/sdks/go.png" height="45px" alt="Go logo">
+- [Apache Beam Java SDK](/documentation/sdks/java) 
+- [Apache Beam Python SDK](/documentation/sdks/python) 
+- [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 Samza <img src="/images/logos/runners/samza.png" height="20px"
alt="Apache Samza logo">
-- Apache Spark 
-- Google Cloud Dataflow 
-- Hazelcast Jet 
-- Twister2 
+- [Direct Runner](/documentation/runners/direct)
+- [Apache Flink Runner](/documentation/runners/flink) 
+- [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) 
+- [Google Cloud Dataflow Runner](/documentation/runners/dataflow) 
+- [Hazelcast Jet Runner](/documentation/runners/jet) 
+- [Twister2 Runner](/documentation/runners/twister2) 
**Note:** You can always execute your pipeline locally for testing and
debugging purposes.
