Automated commit at 2014-09-08 23:21:01 UTC by middleman-deploy 0.2.3

index.html

@@ -85,7 +85,7 @@ <h1>
           <a href="speakers#sallen"><img alt="Sarah Allen" src="/images/speakers/sallen.jpeg" /></a>
             <dl>
               <dt><a href="speakers#sallen">Sarah Allen</a></dt>
-              <!-- <dd>Topic TBD</dd> -->
+              <!-- <dd>3 Reasons Not to Use Ruby</dd> -->
             </dl>
           </div>
 

schedule/index.html

@@ -104,12 +104,12 @@ <h3>Registration</h3>
       <img alt="Sarah Allen" src="/images/speakers/sallen.jpeg" />
 
     <div id="sallen">
-      <h3>TBA</h3>
+      <h3>3 Reasons Not to Use Ruby</h3>
 
           <p>by <a href="/speakers/#sallen">Sarah Allen</a></p>
 
       <p class="session-description">
-        Coming Soon
+        Coming Soon!
       </p>
     </div>
   </td>
@@ -279,15 +279,15 @@ <h3>Taking over Someone Else's Open-Source Projects</h3>
 
       <td>
 
-      <img alt="TBA" src="/images/speakers/person.png" />
+      <img alt="Kiyoto Tamura" src="/images/speakers/ktamura.jpg" />
 
-    <div id="tba">
-      <h3>TBA</h3>
+    <div id="ktamura">
+      <h3>How to become a data scientist with Ruby and Fluentd</h3>
 
-          <p>by <a href="/speakers/#tba">TBA</a></p>
+          <p>by <a href="/speakers/#ktamura">Kiyoto Tamura</a></p>
 
       <p class="session-description">
-        Coming Soon!
+        As someone who once cut teeth in quantitative finance, I can tell you that a huge fraction of 'data science' is data collection. Yes, it's kind of unsexy, but it's hugely important. If you have bad data, you have no chance of drawing good conclusions from your data consistently. That was the thought behind the Fluentd project: we realized that the reality of data collection (especially for log data) is messy, and we wanted to clean it up.<br /><br />In this talk, I plan to code live onstage to show how easy it is to get started with Fluentd by creating a useful data collection pipeline.
       </p>
     </div>
   </td>
@@ -580,12 +580,12 @@ <h3>Registration</h3>
       <img alt="Sarah Allen" src="/images/speakers/sallen.jpeg" />
 
     <div id="sallen">
-      <h3>TBA</h3>
+      <h3>3 Reasons Not to Use Ruby</h3>
 
           <p>by <a href="/speakers/#sallen">Sarah Allen</a></p>
 
       <p class="session-description">
-        Coming Soon
+        Coming Soon!
       </p>
     </div>
   </td>
@@ -933,15 +933,15 @@ <h3>Gilding the Rose: Refactoring Legacy Code</h3>
 
               <td>
 
-      <img alt="TBA" src="/images/speakers/person.png" />
+      <img alt="Kiyoto Tamura" src="/images/speakers/ktamura.jpg" />
 
-    <div id="tba">
-      <h3>TBA</h3>
+    <div id="ktamura">
+      <h3>How to become a data scientist with Ruby and Fluentd</h3>
 
-          <p>by <a href="/speakers/#tba">TBA</a></p>
+          <p>by <a href="/speakers/#ktamura">Kiyoto Tamura</a></p>
 
       <p class="session-description">
-        Coming Soon!
+        As someone who once cut teeth in quantitative finance, I can tell you that a huge fraction of 'data science' is data collection. Yes, it's kind of unsexy, but it's hugely important. If you have bad data, you have no chance of drawing good conclusions from your data consistently. That was the thought behind the Fluentd project: we realized that the reality of data collection (especially for log data) is messy, and we wanted to clean it up.<br /><br />In this talk, I plan to code live onstage to show how easy it is to get started with Fluentd by creating a useful data collection pipeline.
       </p>
     </div>
   </td>

speakers/index.html

@@ -86,7 +86,7 @@ <h2>Sarah Allen</h2>
           <img alt="Sarah Allen" src="/images/speakers/sallen.jpeg" />
           <p class="bio">Sarah is a serial innovator with a history of developing leading-edge products, such as After Effects, Shockwave, Flash video, and OpenLaszlo. She has a habit of recognizing great and timely ideas, finding talented teams, and creating compelling software. She has led small and large teams and confidently turns vision into reality. She runs an innovative product strategy, design & development company, Blazing Cloud, in San Francisco.</p>
           <p>
-           <em class="is-mobile">Session: <a href="/schedule/#mobile_sallen">Topic TBD</a></em><em class="is-not-mobile">Session: <a href="/schedule/#sallen">Topic TBD</a></em>
+           <em class="is-mobile">Session: <a href="/schedule/#mobile_sallen">3 Reasons Not to Use Ruby</a></em><em class="is-not-mobile">Session: <a href="/schedule/#sallen">3 Reasons Not to Use Ruby</a></em>
 
 
             <a href="http://twitter.com/ultrasaurus"><i class="icon-twitter"></i> @ultrasaurus</a>
@@ -146,6 +146,19 @@ <h2>Aja Hammerly</h2>
           </p>
         </div>
 
+        <div id="ktamura" class="speaker-page-item">
+          <h2>Kiyoto Tamura</h2>
+          <img alt="Kiyoto Tamura" src="/images/speakers/ktamura.jpg" />
+          <p class="bio">Kiyoto Tamura is a developer evangelist at Treasure Data. He spends his day (and many nights) evangelizing Fluentd, the open source log collector used as companies like Nintendo and Slideshare to collect data from servers, mobile apps and sensor devices into AWS, Hadoop, Elasticsearch and more.<p> As a math nerd turned a quantitative analyst turned a developer advocate at a big data startup, he has a soft spot for data analytics and statistics. Also, he loves dogs more than cats (sorry tenderlove).</p>
+          <p>
+           <em class="is-mobile">Session: <a href="/schedule/#mobile_ktamura">How to become a data scientist with Ruby and Fluentd</a></em><em class="is-not-mobile">Session: <a href="/schedule/#ktamura">How to become a data scientist with Ruby and Fluentd</a></em>
+
+
+            <a href="http://twitter.com/kiyototamura"><i class="icon-twitter"></i> @kiyototamura</a>
+
+          </p>
+        </div>
+
         <div id="nlong" class="speaker-page-item">
           <h2>Nathan Long</h2>
           <img alt="Nathan Long" src="/images/speakers/nlong.jpg" />