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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>