Author: jbonofre
Date: Fri Feb 12 17:51:32 2016
New Revision: 1730081
URL: http://svn.apache.org/viewvc?rev=1730081&view=rev
Log:
[scm-publish] Updating Beam website
Modified:
incubator/beam/website/index.html
Modified: incubator/beam/website/index.html
URL:
http://svn.apache.org/viewvc/incubator/beam/website/index.html?rev=1730081&r1=1730080&r2=1730081&view=diff
==============================================================================
--- incubator/beam/website/index.html (original)
+++ incubator/beam/website/index.html Fri Feb 12 17:51:32 2016
@@ -156,12 +156,12 @@
<div class="section">
<h3 id="Using_Apache_Beam">Using Apache Beam</h3>
<p>You can use Beam for nearly any kind of data processing task, including
both batch and streaming data processing. Beam provides a unified data model
that can represent any size data set, including an unbounded or infinite data
set from a continuously updating data source such as Kafka.</p>
- <p>In particular, Beam pipelines can represent high-volume computations,
where the steps in your job need to process an amount of data that exceeds the
memory capacity of a cost-effective cluster. Beam is particularly useful for <a
class="externalLink"
href="http://en.wikipedia.org/wiki/Embarassingly_parallel";>Embarrassingly
Parallel</a> data processing tasks, in which the problem can be decomposed into
many smaller bundles of data that can be processed indepently and in
parallel.</p>
- <p>You can also use Beam for Extract, Transform, and Load (ETL) taks 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>In particular, Beam pipelines can represent high-volume computations,
where the steps in your job need to process an amount of data that exceeds the
memory capacity of a cost-effective cluster. Beam is particularly useful for <a
class="externalLink"
href="http://en.wikipedia.org/wiki/Embarassingly_parallel";>Embarrassingly
Parallel</a> data processing tasks, in which the problem can be decomposed into
many smaller bundles of data that can be processed independently and in
parallel.</p>
+ <p>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>
</div>
<div class="section">
<h3 id="Programming_Model">Programming Model</h3>
- <p>Beam provides a simple and elegant <a
href="programming-model.html">programming model</a> to express your data
processing jobs. Each job is represented by a data processing pipeline that you
create by writing a program with Beam. Each pipeline is an independent entity
that reads some input data, performs some transforms on that data to gain
useful or actionable intelligence about it, and produces some resulting output
data. A pipelineâs transform might include filtering, grouping, comparing, or
joining data.</p>
+ <p>Beam provides a simple and elegant <a class="externalLink"
href="https://cloud.google.com/dataflow/model/programming-model";>programming
model</a> to express your data processing jobs. Each job is represented by a
data processing pipeline that you create by writing a program with Beam. Each
pipeline is an independent entity that reads some input data, performs some
transforms on that data to gain useful or actionable intelligence about it, and
produces some resulting output data. A pipelineâs transform might include
filtering, grouping, comparing, or joining data.</p>
<p>Beam provides several useful abstractions that allow you to think about
your data processing pipeline in a simple, logical way. Beam simplifies the
mechanics of large-scale parallel data processing, freeing you from the need to
manage orchestration details such as partitioning your data and coordinating
individual workers.</p>
</div>
<div class="section">
@@ -171,7 +171,7 @@
<li><i>Powerful data transforms</i>. Beam provides several core data
transforms that you can apply to your data. These transforms, called
PTransforms, are generic frameworks that apply functions that you provide
across an entire data set.</li>
<li><i>I/O APIs for a variety of data formats</i>. Beam provides APIs that
let your pipeline read and write data to and from a variety of formats and
storage technologies. Your pipeline can read text files, Avro files, and
more.</li>
</ul>
- <p>See the <a href="programming-model.html">programming model
documentation</a> to lear more about how Beam implements these concepts.</p>
+ <p>See the <a href="programming-model.html">programming model
documentation</a> to learn more about how Beam implements these concepts.</p>
</div>
<div class="section">
<h3 id="News">News</h3>
