learning/katas/python/Introduction/Hello Beam/Hello Beam/task.html - beam - Git at Google

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 <html>
 <h2>Hello Beam Pipeline</h2>
 <p>
   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 Apex, Apache Flink, Apache Spark, and Google Cloud Dataflow.
 </p>
 <p>
   Beam is particularly useful for Embarrassingly 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, or loading data onto a new system.
 </p>
 <p>
   To learn more about Apache Beam, refer to
   <a href="https://beam.apache.org/get-started/beam-overview/">Apache Beam Overview</a>.
 </p>
 <p>
   <b>Kata:</b> Your first kata is to create a simple pipeline that takes a hardcoded input element
   "Hello Beam".
 </p>
 <br>
 <div class="hint">
   Hardcoded input can be created using
   <a href="https://beam.apache.org/releases/pydoc/current/apache_beam.transforms.core.html#apache_beam.transforms.core.Create">
   Create</a>.
 </div>
 <div class="hint">
   Refer to the Beam Programming Guide
   <a href="https://beam.apache.org/documentation/programming-guide/#creating-pcollection-in-memory">
   "Creating a PCollection from in-memory data"</a> section for more information.
 </div>
 </html>
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	<html>
	<h2>Hello Beam Pipeline</h2>
	<p>
	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 Apex, Apache Flink, Apache Spark, and Google Cloud Dataflow.
	</p>
	<p>
	Beam is particularly useful for Embarrassingly 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, or loading data onto a new system.
	</p>
	<p>
	To learn more about Apache Beam, refer to
	<a href="https://beam.apache.org/get-started/beam-overview/">Apache Beam Overview</a>.
	</p>
	<p>
	<b>Kata:</b> Your first kata is to create a simple pipeline that takes a hardcoded input element
	"Hello Beam".
	</p>
	<br>
	<div class="hint">
	Hardcoded input can be created using
	<a href="https://beam.apache.org/releases/pydoc/current/apache_beam.transforms.core.html#apache_beam.transforms.core.Create">
	Create</a>.
	</div>
	<div class="hint">
	Refer to the Beam Programming Guide
	<a href="https://beam.apache.org/documentation/programming-guide/#creating-pcollection-in-memory">
	"Creating a PCollection from in-memory data"</a> section for more information.
	</div>
	</html>