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From chenliang...@apache.org
Subject [51/52] [partial] incubator-carbondata-site git commit: updated website version 1
Date Mon, 26 Dec 2016 12:59:11 GMT
updated website version 1


Project: http://git-wip-us.apache.org/repos/asf/incubator-carbondata-site/repo
Commit: http://git-wip-us.apache.org/repos/asf/incubator-carbondata-site/commit/b28a17be
Tree: http://git-wip-us.apache.org/repos/asf/incubator-carbondata-site/tree/b28a17be
Diff: http://git-wip-us.apache.org/repos/asf/incubator-carbondata-site/diff/b28a17be

Branch: refs/heads/asf-site
Commit: b28a17be21cc532e22c3d1f10d94ad3013cac3b6
Parents: 5bcdcb3
Author: PallaviSingh1992 <pallavisingh_1992@yahoo.co.in>
Authored: Mon Dec 26 18:19:21 2016 +0530
Committer: PallaviSingh1992 <pallavisingh_1992@yahoo.co.in>
Committed: Mon Dec 26 18:19:21 2016 +0530

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 content/blogs.html                              |  210 +
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 content/cd-blog-caseStudyCarbonData.html        |  266 +
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 content/images/CDfullyIndexing.png              |  Bin 16207 -> 16409 bytes
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 content/overview.html                           |  154 -
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 content/slick/slick.less                        |   99 -
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 content/userguideDoc.html                       |  326 +-
 content/usingcarbondata.html                    |  105 -
 content/usingcarbondataDoc.html                 |  362 +-
 src/main/webapp/blogs.html                      |  210 +
 .../webapp/cd-blog-InteractiveAnalysis.html     |  220 +
 .../webapp/cd-blog-caseStudyCarbonData.html     |  266 +
 src/main/webapp/cd-blog-usingCarbonData.html    |  218 +
 src/main/webapp/configuringcarbondataDoc.html   |  243 +
 src/main/webapp/contributingDoc.html            |  251 +-
 src/main/webapp/css/print.css                   |    5 +
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 src/main/webapp/docs/latest/Contribution.html   |  177 +-
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 .../images/format/carbon_data_random_scan.png   |  Bin
 src/main/webapp/docs/latest/installation.html   |  198 +-
 src/main/webapp/docs/latest/overview.html       |  228 +-
 src/main/webapp/docs/latest/quick_start.html    |  164 +-
 .../webapp/docs/latest/troubleshooting.html     |   95 +-
 src/main/webapp/docs/latest/use_cases.html      |  114 +-
 src/main/webapp/docs/latest/useful_tips.html    |  180 +-
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 src/main/webapp/faqDoc.html                     |  328 +-
 src/main/webapp/images/CDfullyIndexing.png      |  Bin 16207 -> 16409 bytes
 src/main/webapp/images/CDfullyIndexing1.png     |  Bin 0 -> 16207 bytes
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 src/main/webapp/index.html                      |  401 +-
 src/main/webapp/installingcarbondataDoc.html    |  237 +
 src/main/webapp/js/custon.js                    |   52 +-
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 src/main/webapp/usefultipsDoc.html              |  334 +-
 src/main/webapp/userguideDoc.html               |  326 +-
 src/main/webapp/usingcarbondataDoc.html         |  362 +-
 214 files changed, 12067 insertions(+), 39036 deletions(-)
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http://git-wip-us.apache.org/repos/asf/incubator-carbondata-site/blob/b28a17be/content/blogs.html
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diff --git a/content/blogs.html b/content/blogs.html
new file mode 100644
index 0000000..2abb4e9
--- /dev/null
+++ b/content/blogs.html
@@ -0,0 +1,210 @@
+<!DOCTYPE html>
+<html lang="en">
+  <head>
+    <meta charset="utf-8">
+    <meta http-equiv="X-UA-Compatible" content="IE=edge">
+    <meta name="viewport" content="width=device-width, initial-scale=1">
+    <!-- The above 3 meta tags *must* come first in the head; any other head content must come *after* these tags -->
+    <title>CarbonData</title>
+
+    <!-- Bootstrap -->
+	
+    <link rel="stylesheet" href="css/bootstrap.min.css">
+    <link href="css/style.css" rel="stylesheet">    	
+    <link href="css/print.css" rel="stylesheet" >   
+    <!-- HTML5 shim and Respond.js for IE8 support of HTML5 elements and media queries -->
+    <!-- WARNING: Respond.js doesn't work if you view the page via file:// -->
+    <!--[if lt IE 9]>
+      <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
+      <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
+    <![endif]-->
+  </head>
+  <body>
+    <header>
+     <nav class="navbar navbar-default navbar-custom cd-navbar-wrapper" >
+      <div class="container">
+        <div class="navbar-header">
+          <button aria-controls="navbar" aria-expanded="false" data-target="#navbar" data-toggle="collapse" class="navbar-toggle collapsed" type="button">
+            <span class="sr-only">Toggle navigation</span>
+            <span class="icon-bar"></span>
+            <span class="icon-bar"></span>
+            <span class="icon-bar"></span>
+          </button>
+          <a href="index.html" class="logo">
+             <img src="images/CarbonDataLogo.png" alt="CarbonData logo" title="CarbocnData logo"  />      
+          </a>
+        </div>
+        <div class="navbar-collapse collapse cd_navcontnt" id="navbar">         
+           <ul class="nav navbar-nav navbar-right navlist-custom">
+              <li><a href="index.html" class="active hidden-xs"><i class="fa fa-home" aria-hidden="true"></i> </a></li>
+              <li><a href="index.html" class="active hidden-lg hidden-md hidden-sm">Home</a></li>
+            <!--  <li><a href="https://github.com/apache/incubator-carbondata">  </a>-->
+               <li class="dropdown">
+                  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="false">Download <span class="caret"></span></a>
+                  <ul class="dropdown-menu">
+                    <li><a href="https://www.apache.org/dyn/closer.lua/incubator/carbondata/0.2.0-incubating"  target="_blank">Apache CarbonData 0.2.0</a></li>
+                    <li><a href="https://www.apache.org/dyn/closer.lua/incubator/carbondata/0.1.1-incubating"  target="_blank">Apache CarbonData 0.1.1</a></li>
+                    <li><a href="https://www.apache.org/dyn/closer.lua/incubator/carbondata/0.1.0-incubating"  target="_blank">Apache CarbonData 0.1.0</a></li>
+                    <li><a href="pageinprogress.html">Release Archive</a></li>
+                    </ul>
+                </li>
+
+               </li>
+              <!--<li><a href="pageinprogress.html">OverView </a></li>-->
+             <!-- <li><a href="dashboard.html">Documentation </a></li>-->
+             <li class="dropdown">
+                  <a href="#" class="dropdown-toggle " data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="false">Documentation <span class="caret"></span></a>
+                  <ul class="dropdown-menu">
+                    <li><a href="quickstartDoc.html">Quick Start</a></li>
+                    <li><a href="overDoc.html">Overview</a></li>
+                    <li><a href="installingcarbondataDoc.html">Installing CarbonData</a></li>
+                    <li><a href="configuringcarbondataDoc.html">Configuring CarbonData</a></li>
+                    <li><a href="usingcarbondataDoc.html">Using CarbonData</a></li>
+                    <li><a href="usefultipsDoc.html">Useful Tips</a></li>
+                    <li><a href="usecasesDoc.html">Use Cases</a></li>
+                    <li><a href="troubleshootingDoc.html">Troubleshooting</a></li>
+                    <li><a href="faqDoc.html">FAQ</a></li>                    
+                    <li><a href="pageinprogress.html">Documentation Archive</a></li>                     
+                    </ul>
+              </li>           
+              <li class="dropdown">
+                  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="false">Community <span class="caret"></span></a>
+                  <!--<ul class="dropdown-menu">
+                    <li><a href="contributingDoc.html"  target="_blank">Contributing to CarbonData</a></li>
+                    <li><a href="http://apache-carbondata-mailing-list-archive.1130556.n5.nabble.com/"  target="_blank">CarbonData Mailing List</a></li>
+                    </ul>-->
+                  <ul class="dropdown-menu">
+                    <li><a href="contributingDoc.html">Contributing to CarbonData</a></li>
+                    <li><a href="contributingDoc.html#Mailing_List" class="list-group-item"
+                           target="target-iframe-contribute"  onclick="frames['target-iframe-contribute'].document.getElementById('Mailing_List').scrollIntoView();return false">Mailing Lists</a>
+                    </li>
+                    <li><a href="contributingDoc.html#Apache_Jira" class="list-group-item"
+                           target="target-iframe-contribute"  onclick="frames['target-iframe-contribute'].document.getElementById('Apache_Jira').scrollIntoView();return false">Issue Tracker</a>
+                    </li>
+                    <li><a href="contributingDoc.html#Committers" class="list-group-item"
+                           target="target-iframe-contribute"  onclick="frames['target-iframe-contribute'].document.getElementById('Committers').scrollIntoView();return false">Project Committers</a>
+                    </li>                                        
+                  </ul>
+
+                </li>
+              <!--<li><a href="http://www.apache.org/" class="hidden-lg hidden-md hidden-sm">Apache</a></li>
+              <li><a href="http://www.apache.org/" class="apache_link hidden-xs" target="blank">apache</a>
+              -->
+              <li class="dropdown">
+                  <a href="http://www.apache.org/" class="apache_link hidden-xs dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="false">Apache</a>
+                   <ul class="dropdown-menu">
+                      <li><a href="http://www.apache.org/"  target="_blank">Apache Homepage</a></li>
+                      <li><a href="http://www.apache.org/licenses/"  target="_blank">License</a></li>
+                      <li><a href="http://www.apache.org/foundation/sponsorship.html"  target="_blank">Sponsorship</a></li>
+                      <li><a href="http://www.apache.org/foundation/thanks.html"  target="_blank">Thanks</a></li>                      
+                    </ul>
+                </li>
+
+                <li class="dropdown">
+                  <a href="http://www.apache.org/" class="hidden-lg hidden-md hidden-sm dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="false">Apache</a>
+                   <ul class="dropdown-menu">
+                      <li><a href="http://www.apache.org/"  target="_blank">Apache Homepage</a></li>
+                      <li><a href="http://www.apache.org/licenses/"  target="_blank">License</a></li>
+                      <li><a href="http://www.apache.org/foundation/sponsorship.html"  target="_blank">Sponsorship</a></li>
+                      <li><a href="http://www.apache.org/foundation/thanks.html"  target="_blank">Thanks</a></li>                      
+                    </ul>
+                </li>
+
+           </ul>
+        </div><!--/.nav-collapse -->
+      </div>
+    </nav>
+     </header> <!-- end Header part -->
+   
+   <div class="fixed-padding"></div> <!--  top padding with fixde header  -->
+ 
+   <section><!-- Dashboard part --> 
+        <div class="container-fluid"> 
+            <div class="dashboardlinkbox">
+               <div class="col-md-12 col-sm-12">
+                  <div>
+                     <h4 class="title-heading">Blog post
+                        <span class="title-underline"></span> 
+                     </h4>
+                  </div>
+                  <article class="cd-blog-box">
+                      <h2 class="title"><a href="cd-blog-caseStudyCarbonData.html">Case Study of CarbonData</a></h2>
+                      <p class="date">23 Dec 2016</p>
+                      <p class="title-info">As an organisation, we were looking for a big data platform to execute the Analytics queries. This is when we were attending one of the Spark meetups and we came to know about CarbonData. So we got interested on exploring CarbonData.</p>
+                      <p><a href="cd-blog-caseStudyCarbonData.html" class="bloglink">Continue reading »</a></p>
+                    </article>
+
+                    <article class="cd-blog-box">
+                      <h2 class="title"><a href="cd-blog-usingCarbonData.html">Using CarbonData</a></h2>
+                      <p class="date">17 Dec 2016</p>
+                      <p class="title-info">As an organisation, we were looking for a big data platform to execute the Analytics queries. This is when we were attending one of the Spark meetups and we came to know about CarbonData. So we got interested on exploring CarbonData.</p>
+                      <p><a href="cd-blog-usingCarbonData.html" class="bloglink">Continue reading »</a></p>
+                    </article>
+
+
+                   <article class="cd-blog-box">
+                      <h2 class="title"><a href="cd-blog-InteractiveAnalysis.html">Interactive Analysis with CarbonData</a></h2>
+                      <p class="date">28 Nov 2016</p>
+                      <p class="title-info">
+                        For interactive analysis to be successful, we require responses faster than seconds. We tried using Apache Impala and Kylin to meet our enterprise needs, but we ended up with queries taking a lot of time.
+                      </p>
+                      <p><a href="cd-blog-InteractiveAnalysis.html" class="bloglink">Continue reading »</a></p>
+                    </article>
+
+
+                    
+
+               </div>
+           </div>
+        </div>     
+   </section><!-- End Dashboard part -->
+
+  <!--
+  <footer>
+    <div class="topcontant">
+      <div class="container-fluid">
+          <div class="col-md-4 col-sm-4">
+            <p class="footext">
+              Apache CarbonData, CarbonData, Apache, the Apache feather logo, and the Apache CarbonData project logo are trademarks of The Apache Software Foundation
+            </p>
+ 
+          </div>
+          <div class="col-md-8 col-sm-8">
+             <ul class="footer-nav">
+              <li><a href="">Site Map</a></li>
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+          <div class="col-md-8 col-sm-8">
+            <p class="copyright-txt">Copyright © 2016. All rights reserved  &nbsp;&nbsp;|&nbsp;&nbsp;
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+
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+                  <a href="#" class="icons"><i class="fa fa-linkedin" aria-hidden="true"></i></a>
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+  </html>
\ No newline at end of file

http://git-wip-us.apache.org/repos/asf/incubator-carbondata-site/blob/b28a17be/content/cd-blog-InteractiveAnalysis.html
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diff --git a/content/cd-blog-InteractiveAnalysis.html b/content/cd-blog-InteractiveAnalysis.html
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+<!DOCTYPE html>
+<html lang="en">
+  <head>
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+    <meta http-equiv="X-UA-Compatible" content="IE=edge">
+    <meta name="viewport" content="width=device-width, initial-scale=1">
+    <!-- The above 3 meta tags *must* come first in the head; any other head content must come *after* these tags -->
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+
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+                    <li><a href="https://www.apache.org/dyn/closer.lua/incubator/carbondata/0.1.1-incubating"  target="_blank">Apache CarbonData 0.1.1</a></li>
+                    <li><a href="https://www.apache.org/dyn/closer.lua/incubator/carbondata/0.1.0-incubating"  target="_blank">Apache CarbonData 0.1.0</a></li>
+                    <li><a href="pageinprogress.html">Release Archive</a></li>
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+                    <li><a href="quickstartDoc.html">Quick Start</a></li>
+                    <li><a href="overDoc.html">Overview</a></li>
+                    <li><a href="installingcarbondataDoc.html">Installing CarbonData</a></li>
+                    <li><a href="configuringcarbondataDoc.html">Configuring CarbonData</a></li>
+                    <li><a href="usingcarbondataDoc.html">Using CarbonData</a></li>
+                    <li><a href="usefultipsDoc.html">Useful Tips</a></li>
+                    <li><a href="usecasesDoc.html">Use Cases</a></li>
+                    <li><a href="troubleshootingDoc.html">Troubleshooting</a></li>
+                    <li><a href="faqDoc.html">FAQ</a></li>                    
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+ 
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+            <div class="col-md-12 col-sm-12">
+                <div class="blog-content">
+                     <h4 class="title-heading">Interactive Analysis with CarbonData
+                        <span class="title-underline"></span> 
+                     </h4>
+
+                     <h2>Challenges faced in Big Data world</h2>
+                     <p>In the current Big data world, the major challenges that we are facing include</p>
+                     <ul>
+                        <li> The queries are executed on TBs and PBs of data which involve large number of columns in the tables</li>
+                        <li> Queries include filters on multiple columns</li>
+                        <li> There are scenarios that involve aggregate expressions.</li>
+                        <li> Slow response time on queries with filters and aggregations</li>
+                    </ul>
+                    <p>For interactive analysis to be successful, we require responses faster than seconds. We tried using Apache Impala and Kylin to meet our enterprise needs, but we ended up with queries taking a lot of time. Apache Spark SQL also did not fit well into our domain because of being structural in nature, while bulk of our data was Nosql in nature. The nature of our enterprise required us to provide results to queries which generated out of result of previously generated queries.(adhoc in nature)
+                    </p>
+
+                    <h2>How we were trying to meet our requirements</h2>
+                     <p>Initially we deployed three different engines to run different types of queries with efficiency. We deployed </p>
+                     <ul>
+                        <li> Hive for batch queries,</li>
+                        <li> Impala and Spark SQL for Interactive Queries and</li>
+                        <li> HBase for Operational Queries.</li>
+                    </ul>
+                    <p>The biggest challenge before us was each of these engines required us to store data for them individually. This led to inflated cost of storing duplicate data along with the cost involved for separate processing of data in each of these engines. Storing data was not the only challenge for each of the engine, the conversion required in order to make data suitable for each engine itself was a process with cost and time. This cost also escalated due to hardware requirements for providing support to each of these engine and running them in parallel was a costly affair needing several nodes etc.    
+                    </p>
+                     <h2>How CarbonData helped us</h2>
+                     <p>CarbonData is an application that is suitable for all 3 types of queries – sequential, interactive and Random access queries. It enabled us to ingest large volume of structured and unstructured data for analysis based on adhoc queries.
+CarbonData addressed the issue for us by providing a data storage format which provides equal efficiency to all use cases and eliminates the need for data duplicity for each engine. CarbonData also resolved the issue of performance by proving us sub-second responses to all query scenarios. </p>
+                   
+                   <h2>Features of CarbonData that satisfied our requirements</h2>
+                     <p>Using CarbonData the difference in the query performance was very prominent. The query performance of our system was enhanced several times because of the following CarbonData features:</p>
+                     <ul>
+                        <li> Data are stored along MDK (multi-dimensional keys). Data is stored as index in columnar format.</li>
+                        <li> Multiple columns form a column group which saves implicit joins and also saves stitching cost for reconstructing row.</li>
+                        <li>Table level Global dictionary facilitates speedup aggregation, reduce run-time memory footprint, and enable deferred decoding.</li>
+                    </ul>
+
+                    <h2>Conclusion</h2>
+                    <p> In the interactive OLAP query analysis where quick response is very essential, CarbonData has proved to be very essential and very effective.   
+                    </p>
+
+
+
+                  </div>
+                </div>
+            </div>     
+   </section><!-- End Dashboard part -->
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+            </p>
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\ No newline at end of file

http://git-wip-us.apache.org/repos/asf/incubator-carbondata-site/blob/b28a17be/content/cd-blog-caseStudyCarbonData.html
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+<!DOCTYPE html>
+<html lang="en">
+  <head>
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+    <meta http-equiv="X-UA-Compatible" content="IE=edge">
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+
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+                    <li><a href="https://www.apache.org/dyn/closer.lua/incubator/carbondata/0.1.1-incubating"  target="_blank">Apache CarbonData 0.1.1</a></li>
+                    <li><a href="https://www.apache.org/dyn/closer.lua/incubator/carbondata/0.1.0-incubating"  target="_blank">Apache CarbonData 0.1.0</a></li>
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+                    <li><a href="overDoc.html">Overview</a></li>
+                    <li><a href="installingcarbondataDoc.html">Installing CarbonData</a></li>
+                    <li><a href="configuringcarbondataDoc.html">Configuring CarbonData</a></li>
+                    <li><a href="usingcarbondataDoc.html">Using CarbonData</a></li>
+                    <li><a href="usefultipsDoc.html">Useful Tips</a></li>
+                    <li><a href="usecasesDoc.html">Use Cases</a></li>
+                    <li><a href="troubleshootingDoc.html">Troubleshooting</a></li>
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+                    </li>                                        
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+
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+                      <li><a href="http://www.apache.org/foundation/thanks.html"  target="_blank">Thanks</a></li>                      
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+            <div class="col-md-12 col-sm-12">
+                <div class="blog-content">
+                     <h4 class="title-heading">Case Study of CarbonData (Query)
+                        <span class="title-underline"></span> 
+                     </h4>
+
+                     <p>In this era of BigData, traditional data processing applications are inadequate to deal with the challenges of analysis, capture, data curation, search, sharing, storage, transfer, visualization, querying, updating, and information privacy.</p>
+
+                     <p>In most of our applications we are gathering data from various sources like  transactional data derived from user’s interactions with the application, data collected from machine sensors, sales data, data from social media or collected results of scientific experiments. These data can be structured like SQL database store or JSON data, as well as unstructured data like documents and streaming data from sensors. Since this data is being generated constantly over a period of time the data become enormous and special techniques are needed to handle this amount of data.</p>
+
+                     <p>For us as an organization this data is very important as it gives insight into a lot of things e.g. root  causes of failures, user habits, frauds, sales figures , geographies , service disruption etc.. But this data is totally useless if we can not analyse this data quickly and draw conclusions. Hence analysing this data has become prime need for us.</p>
+
+                     <p>This need have resulted in a slew of open source innovation in Big Data tools and technologies to collect, store and analyse the high volume, high velocity and high veracity of data.  There are various tools available in the open source that helps us to store and ingest huge amount of data (In petabytes) as well as analysis on them by executing different set of queries like Analytical Query, Batch Query, Operational Query and so on.</p>
+
+                     <p>Most often our use cases define which specific set of data we need and then we will write different kind of queries for the same. Some examples of Use Cases and queries needed to get data for these Use cases are as follow </p>
+                     <ol>
+                        <li><strong>Operational Queries (Random Access)</strong>:  In an E-Commerce application we want to display all the orders placed by a particular user (see Figure 1).
+
+                         <!-- <img src="" alt="" />-->
+
+                         <br />
+                        In this case there may be million of rows but we are mainly focused on data for our current logged in user, so we are interested in a specific set of rows that meet the criteria (user_id == <current_user>) . These kinds of queries are Operational Queries which involved certain rows based on a particular filter.  In big data world HBASE is one of the technologies that is being used for these kind of queries.
+                         </li>
+                        <li><strong>Batch Queries (Sequential Access)</strong>: In the same E-Commerce Application we want to generate a report which contains the number of orders per user, the amount of money spent by that user, number of returns, number of coupons used and monthly orders (see Figure 2). For this use case we have to access all the rows and then aggregate or reduce the data to generate a report.
+                           <!-- <img src="" alt="" />-->
+
+                           <br />
+                           Most of the Business Intelligence (BI) tools execute these queries. Earlier we used to use the Data Warehouse technology  which was specifically developed for these needs in relational world. So in Batch Queries we are focused on the data from all or majority of rows, the number of columns involved might only be few. Apache Hive is quite popular for these kinds of queries.      
+
+                        </li>
+                        <li> <strong> Interactive Analysis Queries (OLAP Queries) </strong>: Taking an example of same application we have a need to provide analysis regarding Users, Items, Sales, Warehouse Inventory for the Items, Sizes Available, Vendor Information and so on (see Figure 3). In this use case we need to query multiple tables, filter multiple columns as well as have aggregate functions or nested queries. Such queries are often generated by Interactive Analysis tools like Tableau, PentaHo. Even for graph databases which provide excellent fast retrieval of OLTP queries we have to use a Big Data tool for OLAP. There are a lot of tools available in the market for these kinds of queries like Apache Impala and Apache Kylin.
+                          <!-- <img src="" alt="" />-->
+                        </li>
+                    </ol>                      
+                    
+
+                    <h2>Challenges Faced</h2>
+                     <p>As you observe in the above section there is a specific tool available for these kind of queries and we can use them to solve the problems so what is the issue here?  </p>
+                     <p>Primarily these are the challenges that we faced.  </p>
+                     <ul>
+                        <li>Different tool needed for different type of queries e.g. HBASE is suited for random access queries and Hive for batch queries.</li>
+                        <li> Data replication: Deploying multiple datastores/engines means that the same data has to be stored in all these engines.</li>
+                        <li> High Cost: Since we are replicating the data to meet our generic needs there is a whole lot of overhead that we are bearing in terms of storage, processing power as well as maintenance that adds to the cost. </li>
+                        <li> Maintenance: Managing and maintaining all the different frameworks is quite a headache. </li>
+                        <li> Excessive IO scans and excessive utilization of CPU resources in above mentioned frameworks which de-accelerated the query performance by several times.  </li>
+                        <li> Space optimization and querying on encoded data without decoding the data until the last stage was another challenge. </li>
+                        <li>Reducing the row construction cost. </li>
+                    </ul>
+
+                    <p>So we researched about a solution that can define a  Data format for all our specific needs and all type of queries can be executed on the same framework with same efficiency.   
+                    </p>
+                     <h2>Is CarbonData the solution ?</h2>
+                     <p>CarbonData was envisaged to solve this problem, it provides a way to create a single data format for all kind of queries and analysis on Big Data. Apache CarbonData can provide blazing fast performance for any query scenario described above using a single storage format. What makes CarbonData so special is, its storage format, which is designed and developed from the ground up to provide fast response for any query scenario.</p>
+                     <p>
+                      Older file formats like Parquet and ORC in the Hadoop eco-system fail to cater equal efficiency to all domains of query like OLAP, Sequential and Random. While working with these two, we found them to be working suitably for big scans. Also they support HDFS to allow levering of existing Hadoop Cluster, but they have been found to be unsuitable in providing the sub second responses for primary key lookups, Olap style queries over big data involving filters and fetching of all columns of record. For Primary Key based fetching we will need to have indexes and that is one of the key features of CarbonData. </p>
+                   
+                   
+                     <p>Following are the prominent features of CarbonData.</p>
+                     <ul>
+                        <li> <strong>Global Dictionary with Deferred Decoding</strong>:- Allows the query engine to perform computations such as Group BY/Aggregation and Sort using encoded values without having to decode each and every row. The decoding needs to be done only for the final query result which in most cases contains much less rows than the scanned data. This helps in Batch queries and Interactive queries. 
+                        </li>
+                        <li> <strong>Index (MDK,Min-Max,Inverted Index)</strong>:- Enables the scanner to quickly locate the required rows without having to read and compare each and every row against the query predicates(filters). This saves a lot in I/O bandwidth and time. This helps in Interactive Queries and Operational Queries.</li>
+                        <li><strong>Column Groups</strong>:-Enables CarbonData to provide row-store like query performance for wide table queries in spite of CarbonData being a columnar data storage format. It helps eliminate the implicit join which is otherwise required to reconstruct rows from columnar storage format. This helps in making Operational Queries several magnitude faster.</li>
+                       <li><strong>Advanced Push Down Optimizations</strong>: Carbon pushes as much of query processing as possible close to the data to minimize the amount of data being read, processed, converted and transmitted/shuffled.
+
+                             <ul>
+                              <li><strong>Projection and Filters</strong>: Since carbon uses columnar format, it reads only the required columns from the store and also reads only the rows that match the filter conditions provided in the query.</li>
+
+                             </ul>
+
+
+                        </li>
+                    </ul>
+
+                    <h2>CarbonData Performance </h2>
+                    <p>Following figures show comparison of other tools with Carbon Data:-
+                          <!-- <img src="" alt="" />-->
+                              <!-- <img src="" alt="" />-->
+                                   </p>
+
+                    <h2>Current Users of CarbonData </h2>
+                    <p>Hulu is one of the first users of CarbonData, It is a North American video industry Internet Company. They have switched over CarbonData to speed up their systems by utilising the features of CarbonData. They filter out 2% to 5% of data for aggregation, the filters are majority based on 5 to 10 columns and the result set has 100+ columns. The finger grained index, columnar groups provided in CarbonData render speedier results than Parquet/ ORC in this Use Case.  
+                     </p>
+                                   
+
+                    <h2>Conclusion</h2>
+                    <p>CarbonData provides features that are very useful to us like low latency, space efficiency, rapid processing and intelligent solutions to BigData problems. CarbonData’s indexing significantly accelerates query performance and reduces the I/O scans and CPU resources, where there are filters in the query. CarbonData index consists of multiple level of indices allowing a processing framework to leverage on these  index(s) to reduce the task it needs to schedule and process, and also supports skip scan in more finer grain unit (called blocklet) in task side scanning instead of scanning the whole file. CarbonData by supporting efficient compression and global encoding schemes, allows queries on compressed/encoded data, the data can be converted just before returning the results to the users, which is "late materialized". It allow multiple columns to form a column group that would be stored as row format. This reduces the row reconstruction cost at query time. </p
 >              
+
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+                    <li><a href="usingcarbondataDoc.html">Using CarbonData</a></li>
+                    <li><a href="usefultipsDoc.html">Useful Tips</a></li>
+                    <li><a href="usecasesDoc.html">Use Cases</a></li>
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+                <div class="blog-content">
+                     <h4 class="title-heading">Using CarbonData
+                        <span class="title-underline"></span> 
+                     </h4>
+
+                     <p>As an organisation, we were looking for a big data platform to execute the Analytics queries. This is when we were attending one of the Spark meetups and we came to know about CarbonData. So we got interested on exploring CarbonData.</p>
+                     <p>First step was to to install and use the Carbon Data application. Installing, building and using Carbon Data is simple, even for those who are new to the Big Data Technology. We started with the Quick Start installation given in the Carbon Data online documentation. The directions given in the quick start were pretty intuitive. Within no time we were able to do the entire setup and started executing some basic queries. The directions involved the following steps:-</p>
+
+                     <ol>   
+                        <li>Download and install Spark1.5.0 to Spark 1.6.2</li>
+                        <li>Download and install Apache Thrift 0.9.3</li>
+                        <li>Download Apache CarbonData code using the following command<br>
+                          $ git clone <a href="https://github.com/apache/incubator-carbondata.git">click here</a> 
+                        </li>
+                        <li> 4. Build the downloaded CarbonData code using the following command<br>
+                            $ mvn -DskipTests -Pspark-1.6 -Dspark.version=1.6.2 clean package</li>
+                    </ol>
+                    <p>Creating the table was the first step. So, we used the Create table query to create a table with a mix of dimensions and measures.<br>
+Next, we used a CSV file to load the data from. We used the Load query next.<br>
+After the successful loading of data, we executed some basic select queries.
+                    </p>
+
+                    
+                     <p>With the quick installation so easy and intuitive, we wanted to explore more. We thought of using the CarbonData in a scenario similar to the real world. The next step was to do the CarbonData setup on the Standalone Spark cluster. After following the online documentation and with the help from their community, we were able to complete the setup.</p>
+                     <p>As we are already familiar with Parquet and Hive, we tried to compare query performance between CarbonData and Parquet. We tested the performance on 2 systems with the following setup:-</p>
+                     <ul>
+                        <li> 500 GB data</li>
+                        <li> 300 columns</li>
+                        <li> Select query on 150 columns</li>
+                        <li> Cluster setup – 5 nodes, each node having 4 core and  8 GB </li>
+                    </ul>
+                    <p>We loaded different sizes of data in the table – 10 GB, 100 GB, 500GB, 5 TB, 300 columns. For each data, the query performance was much better with CarbonData. With the increase in the data size, the difference in the query performance was even more visible.   
+                    </p>
+                     
+                     <p>Ease of loading the data was easily noticeable. The data can be loaded in the Carbon tables from a variety of sources. Carbon Data supports loading data from different files, from Parquet and Hive table, using Dataframes, and from JSON.</p>
+                  
+                    <p> Another noticeable feature was the support for the varied data types. Along with the all basic data types, CarbonData supports some complex ones as well, for e.g. Array, struct complex nested types.  
+                    </p>
+
+
+
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