Repository: incubator-spot
Updated Branches:
  refs/heads/asf-site bbc11dab3 -> 1ea11648a


Fixing Inconsistent/incorrect use of Apache trademark on the blog entry: How 
Apache Spot (Incubating) Helps Create Well-Stocked Data Lakes and Catch 
Powerful Insights


Project: http://git-wip-us.apache.org/repos/asf/incubator-spot/repo
Commit: http://git-wip-us.apache.org/repos/asf/incubator-spot/commit/1ea11648
Tree: http://git-wip-us.apache.org/repos/asf/incubator-spot/tree/1ea11648
Diff: http://git-wip-us.apache.org/repos/asf/incubator-spot/diff/1ea11648

Branch: refs/heads/asf-site
Commit: 1ea11648a8bf512671d0f9ce561c25d7eec5b6d9
Parents: bbc11da
Author: cesar <ce...@apache.org>
Authored: Mon Oct 17 18:05:19 2016 -0500
Committer: cesar <ce...@apache.org>
Committed: Mon Oct 17 18:05:19 2016 -0500

----------------------------------------------------------------------
 .../index.html                                  | 22 ++++++++++----------
 1 file changed, 11 insertions(+), 11 deletions(-)
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http://git-wip-us.apache.org/repos/asf/incubator-spot/blob/1ea11648/how-apache-spot-helps-create-well-stocked-data-lakes-and-catch-powerful-insights/index.html
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diff --git 
a/how-apache-spot-helps-create-well-stocked-data-lakes-and-catch-powerful-insights/index.html
 
b/how-apache-spot-helps-create-well-stocked-data-lakes-and-catch-powerful-insights/index.html
index 4249e9e..2d2b725 100644
--- 
a/how-apache-spot-helps-create-well-stocked-data-lakes-and-catch-powerful-insights/index.html
+++ 
b/how-apache-spot-helps-create-well-stocked-data-lakes-and-catch-powerful-insights/index.html
@@ -31,9 +31,9 @@
 
         <link rel='dns-prefetch' href='//fonts.googleapis.com' />
         <link rel='dns-prefetch' href='//s.w.org' />
-        <link rel="alternate" type="application/rss+xml" title="Apache Spot 
&raquo; Feed" href="../feed/" />
-        <link rel="alternate" type="application/rss+xml" title="Apache Spot 
&raquo; Comments Feed" href="../comments/feed/" />
-        <link rel="alternate" type="application/rss+xml" title="Apache Spot 
&raquo; How Apache Spot Helps Create Well-Stocked Data Lakes and Catch Powerful 
Insights Comments Feed" 
href="../how-open-network-insight-helps-create-well-stocked-data-lakes-and-catch-powerful-insights/feed/"
 />
+        <link rel="alternate" type="application/rss+xml" title="Apache Spot 
(Incubating) &raquo; Feed" href="../feed/" />
+        <link rel="alternate" type="application/rss+xml" title="Apache Spot 
(Incubating) &raquo; Comments Feed" href="../comments/feed/" />
+        <link rel="alternate" type="application/rss+xml" title="Apache Spot 
(Incubating) &raquo; How Apache Spot (Incubating) Helps Create Well-Stocked 
Data Lakes and Catch Powerful Insights Comments Feed" 
href="../how-open-network-insight-helps-create-well-stocked-data-lakes-and-catch-powerful-insights/feed/"
 />
         <script type="text/javascript">
                        window._wpemojiSettings = {
                                "baseUrl" : 
"https:\/\/s.w.org\/images\/core\/emoji\/2\/72x72\/",
@@ -196,27 +196,27 @@
                                     About four years ago, the era of the Big 
Data analytics began. Paired with advanced analytics, massive volumes of data 
can be culled to not only inform critical decisions, but also to simulate 
sophisticated “what if” scenarios that allow companies to gain competitive 
advantages by generating and predicting different scenarios. For example, a 
financial services company can more accurately determine what other products to 
offer a customer, and in what order, based on a wide variety of data, then use 
advanced analytics to gather insights. Creating a data lake that can be 
effectively used for predictive analytics raises tough questions — what data 
sources should we use?  How should this data be collected and ingested? What 
are the best algorithms to analyze the data, and how should we present these 
results to our decision maker?
                                 </p>
                                 <p>
-                                    Apache Spot can help to solve most of 
these issues. Following is a description of the Apache Spot, which is designed 
to facilitate Big Data analytics scenarios like the financial services 
company’s question about the right product to offer customers.
+                                    Apache Spot (Incubating) can help to solve 
most of these issues. Following is a description of the Apache Spot 
(Incubating), which is designed to facilitate Big Data analytics scenarios like 
the financial services company’s question about the right product to offer 
customers.
                                 </p>
                                 <a 
href="../wp-content/uploads/2016/09/ONI_Architecture-Diagram_1300_v4.png"><img 
src="../wp-content/uploads/2016/09/ONI_Architecture-Diagram_1300_v4.png" 
alt="oni_architecture-diagram_1300_v4" width="1300" height="675" 
class="alignnone size-full wp-image-114" 
srcset="../wp-content/uploads/2016/09/ONI_Architecture-Diagram_1300_v4.png 
1300w, 
../wp-content/uploads/2016/09/ONI_Architecture-Diagram_1300_v4-300x156.png 
300w, 
../wp-content/uploads/2016/09/ONI_Architecture-Diagram_1300_v4-768x399.png 
768w, 
../wp-content/uploads/2016/09/ONI_Architecture-Diagram_1300_v4-1024x532.png 
1024w" sizes="(max-width: 1300px) 100vw, 1300px" /></a>
-                                <h3><strong>Apache Spot Core 
Components</strong></h3>
+                                <h3><strong>Apache Spot (Incubating) Core 
Components</strong></h3>
                                 <p>
-                                    The Apache Spot Core is composed of three 
main components — data integration (collectors), data store (HDFS here, but 
can also be a non-SQL database) and machine learning.
+                                    The Apache Spot (Incubating) Core is 
composed of three main components — data integration (collectors), data store 
(HDFS here, but can also be a non-SQL database) and machine learning.
                                 </p>
                                 <p>
-                                    In this diagram, the top left shows Apache 
Spot Data Sources, which include the collection of the information that will be 
used to create a data lake. The process is simple. Define a pull or push from 
the source of information then capture this information on Apache Spot’s 
“collectors.” The collectors are processes that interpret the information 
that is sent, then write it to the HDFS system in the Apache Spot cluster. The 
HDFS stores the data lake and ensures that resources can grow while remaining 
economical at every size. The Apache Spot algorithms are part of machine 
learning and are used to detect the uncommon information in the data lake.
+                                    In this diagram, the top left shows Apache 
Spot (Incubating) Data Sources, which include the collection of the information 
that will be used to create a data lake. The process is simple. Define a pull 
or push from the source of information then capture this information on Apache 
Spot (Incubating)’s “collectors.” The collectors are processes that 
interpret the information that is sent, then write it to the HDFS system in the 
Apache Spot (Incubating) cluster. The HDFS stores the data lake and ensures 
that resources can grow while remaining economical at every size. The Apache 
Spot (Incubating) algorithms are part of machine learning and are used to 
detect the uncommon information in the data lake.
                                 </p>
                                 <h3><strong>Operational Analytics</strong></h3>
                                 <p>
-                                    As part of operational analytics, Apache 
Spot executes different batch processes that add information to machine 
learning results to provide meaning and context. Using the financial services 
product example, basic customer data could be augmented with information about 
other customers in the same region along with information about which products 
those customers recommended or complained about. Basically, the data scientists 
can “play” with the data using different algorithms to identify insights.
+                                    As part of operational analytics, Apache 
Spot (Incubating) executes different batch processes that add information to 
machine learning results to provide meaning and context. Using the financial 
services product example, basic customer data could be augmented with 
information about other customers in the same region along with information 
about which products those customers recommended or complained about. 
Basically, the data scientists can “play” with the data using different 
algorithms to identify insights.
                                 </p>
                                 <h3><strong>Visualizing Results</strong></h3>
                                 <p>
-                                    The Apache Spot GUI displays the results 
that the machine learning algorithms generate. Results are represented such 
that it is easy to identify both the most common things as well as find the 
most suspicious or uncommon information that is part of the data lake.
+                                    The Apache Spot (Incubating) GUI displays 
the results that the machine learning algorithms generate. Results are 
represented such that it is easy to identify both the most common things as 
well as find the most suspicious or uncommon information that is part of the 
data lake.
                                 </p>
                                 <h3><strong>Customizable Open 
Source</strong></h3>
                                 <p>
-                                    Because Apache Spot is an open-source 
project, most of the components depicted here can be modified by the end user.
+                                    Because Apache Spot (Incubating) is an 
open-source project, most of the components depicted here can be modified by 
the end user.
                                 </p>
                             </section>
                             <footer class="article-footer">
@@ -275,7 +275,7 @@
 
                     <nav role="navigation"></nav>
                     <p class="source-org copyright" style="text-align:center;">
-                        &copy; 2016 Apache Spot.
+                        &copy; 2016 Apache Spot (Incubating).
                     </p>
 
                 </div>

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