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+      <h1 class="post-title">Running SQL Queries on Amazon S3</h1>
+    </div>
+    <p class="post-meta">
+    
+      
+      
+      <strong>Author:</strong> Nick Amato (Director, Technical Marketing, MapR 
Technologies)
+    
+<br/><strong>Date:</strong> Dec 9, 2014
+</p>
+  </header>
+  <div class="addthis_sharing_toolbox"></div>
+
+  <article class="post-content">
+    <p>The functionality and sheer usefulness of Drill is growing fast.  If 
you&#39;re a user of some of the popular BI tools out there like Tableau or SAP 
Lumira, now is a good time to take a look at how Drill can make your life 
easier, especially if  you&#39;re faced with the task of quickly getting a 
handle on large sets of unstructured data.  With schema generated on the fly, 
you can save a lot of time and headaches by running SQL queries on the data 
where it rests without knowing much about columns or formats.  There&#39;s even 
more good news:  Drill also works with data stored in the cloud.  With a few 
simple steps, you can configure the S3 storage plugin for Drill and be off to 
the races running queries.  In this post we&#39;ll look at how to configure 
Drill to access data stored in an S3 bucket.</p>
+
+<p>If you&#39;re more of a visual person, you can skip this article entirely 
and <a href="https://www.youtube.com/watch?v=w8gZ2nn_ZUQ";>go straight to a 
video</a> I put together that walks through an end-to-end example with Tableau. 
 This example is easily extended to other BI tools, as the steps are identical 
on the Drill side.</p>
+
+<p>At a high level, configuring Drill to access S3 bucket data is accomplished 
with the following steps on each node running a drillbit.</p>
+
+<ul>
+<li>Download and install the <a href="http://www.jets3t.org/";>JetS3t</a> JAR 
files and enable them.</li>
+<li>Add your S3 credentials in the relevant XML configuration file.</li>
+<li>Configure and enable the S3 storage plugin through the Drill web 
interface.</li>
+<li>Connect your BI tool of choice and query away.</li>
+</ul>
+
+<p>Consult the <a 
href="https://cwiki.apache.org/confluence/display/DRILL/Architectural+Overview";>Architectural
 Overview</a> for a refresher on the architecture of Drill.</p>
+
+<h3 id="prerequisites">Prerequisites</h3>
+
+<p>These steps assume you have a <a 
href="https://cwiki.apache.org/confluence/display/DRILL/Apache+Drill+in+10+Minutes";>typical
 Drill cluster and ZooKeeper quorum</a> configured and running.  To access data 
in S3, you will need an S3 bucket configured and have the required Amazon 
security credentials in your possession.  An <a 
href="http://blogs.aws.amazon.com/security/post/Tx1R9KDN9ISZ0HF/Where-s-my-secret-access-key";>Amazon
 blog post</a> has more information on how to get these from your account.</p>
+
+<h3 id="configuration-steps">Configuration Steps</h3>
+
+<p>To connect Drill to S3, all of the drillbit nodes will need to access code 
in the JetS3t library developed by Amazon.  As of this writing, 0.9.2 is the 
latest version but you might want to check <a 
href="https://jets3t.s3.amazonaws.com/toolkit/toolkit.html";>the main page</a> 
to see if anything has been updated.  Be sure to get version 0.9.2 or later as 
earlier versions have a bug relating to reading Parquet data.</p>
+<div class="highlight"><pre><code class="language-bash" data-lang="bash">wget 
http://bitbucket.org/jmurty/jets3t/downloads/jets3t-0.9.2.zip
+cp jets3t-0.9.2/jars/jets3t-0.9.2.jar <span 
class="nv">$DRILL_HOME</span>/jars/3rdparty
+</code></pre></div>
+<p>Next, enable the plugin by editing the file:</p>
+<div class="highlight"><pre><code class="language-bash" data-lang="bash"><span 
class="nv">$DRILL_HOME</span>/bin/hadoop_excludes.txt
+</code></pre></div>
+<p>and removing the line <code>jets3t</code>.</p>
+
+<p>Drill will need to know your S3 credentials in order to access data there. 
These credentials will need to be placed in the core-site.xml file for your 
installation.  If you already have a core-site.xml file configured for your 
environment, add the following parameters to it, otherwise create the file from 
scratch.  If you do end up creating it from scratch you will need to wrap these 
parameters with <code>&lt;configuration&gt;</code> and 
<code>&lt;/configuration&gt;</code>.</p>
+<div class="highlight"><pre><code class="language-xml" data-lang="xml"><span 
class="nt">&lt;property&gt;</span>
+  <span class="nt">&lt;name&gt;</span>fs.s3.awsAccessKeyId<span 
class="nt">&lt;/name&gt;</span>
+  <span class="nt">&lt;value&gt;</span>ID<span class="nt">&lt;/value&gt;</span>
+<span class="nt">&lt;/property&gt;</span>
+
+<span class="nt">&lt;property&gt;</span>
+  <span class="nt">&lt;name&gt;</span>fs.s3.awsSecretAccessKey<span 
class="nt">&lt;/name&gt;</span>
+  <span class="nt">&lt;value&gt;</span>SECRET<span 
class="nt">&lt;/value&gt;</span>
+<span class="nt">&lt;/property&gt;</span>
+
+<span class="nt">&lt;property&gt;</span>
+  <span class="nt">&lt;name&gt;</span>fs.s3n.awsAccessKeyId<span 
class="nt">&lt;/name&gt;</span>
+  <span class="nt">&lt;value&gt;</span>ID<span class="nt">&lt;/value&gt;</span>
+<span class="nt">&lt;/property&gt;</span>
+
+<span class="nt">&lt;property&gt;</span>
+  <span class="nt">&lt;name&gt;</span>fs.s3n.awsSecretAccessKey<span 
class="nt">&lt;/name&gt;</span>
+  <span class="nt">&lt;value&gt;</span>SECRET<span 
class="nt">&lt;/value&gt;</span>
+<span class="nt">&lt;/property&gt;</span>
+</code></pre></div>
+<p>The steps so far give Drill enough information to connect to the S3 
service.  Remember, you have to do this on all the nodes running drillbit.</p>
+
+<p>Next, let&#39;s go into the Drill web interface and enable the S3 storage 
plugin.  In this case you only need to connect to <strong>one</strong> of the 
nodes because Drill&#39;s configuration is synchronized across the cluster.  
Complete the following steps:</p>
+
+<ol>
+<li>Point your browser to <code>http://&lt;host&gt;:8047</code></li>
+<li>Select the &#39;Storage&#39; tab.</li>
+<li>A good starting configuration for S3 can be entirely the same as the 
<code>dfs</code> plugin, except the connection parameter is changed to 
<code>s3://bucket</code>.  So first select the <code>Update</code> button for 
<code>dfs</code>, then select the text area and copy it into the clipboard (on 
Windows, ctrl-A, ctrl-C works).</li>
+<li>Press <code>Back</code>, then create a new plugin by typing the name into 
the <code>New Storage Plugin</code>, then press <code>Create</code>.  You can 
choose any name, but a good convention is to use 
<code>s3-&lt;bucketname&gt;</code> so you can easily identify it later.</li>
+<li>In the configuration area, paste the configuration you just grabbed from 
&#39;dfs&#39;.  Change the line <code>connection: &quot;file:///&quot;</code> 
to <code>connection: &quot;s3://&lt;bucket&gt;&quot;</code>.</li>
+<li>Click <code>Update</code>.  You should see a message that indicates 
success.</li>
+</ol>
+
+<p>At this point you can run queries on the data directly and you have a 
couple of options on how you want to access it.  You can use Drill Explorer and 
create a custom view (based on an SQL query) that you can then access in 
Tableau or other BI tools, or just use Drill directly from within the tool.</p>
+
+<p>You may want to check out the <a 
href="http://www.youtube.com/watch?v=jNUsprJNQUg";>Tableau demo</a>.</p>
+
+<p>With just a few lines of configuration, you&#39;ve just opened the vast 
world of data available in the Amazon cloud and reduced the amount of work you 
have to do in advance to access data stored there with SQL.  There are even 
some <a href="https://aws.amazon.com/datasets";>public datasets</a> available 
directly on S3 that are great for experimentation.</p>
+
+<p>Happy Drilling!</p>
+
+  </article>
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http://git-wip-us.apache.org/repos/asf/drill-site/blob/c4de0f83/blog/2014/12/11/apache-drill-qa-panelist-spotlight/index.html
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+    <div class="int_title">
+      <h1 class="post-title">Apache Drill Q&A Panelist Spotlight</h1>
+    </div>
+    <p class="post-meta">
+    
+      
+      
+      <strong>Author:</strong>  (, )
+    
+
+</p>
+  </header>
+  <div class="addthis_sharing_toolbox"></div>
+
+  <article class="post-content">
+    <script type="text/javascript" 
src="https://addthisevent.com/libs/1.5.8/ate.min.js";></script>
+
+<p><a href="/blog/2014/12/11/apache-drill-qa-panelist-spotlight/" title="Add 
to Calendar" class="addthisevent">
+    Add to Calendar
+    <span class="_start">12-17-2014 11:30:00</span>
+    <span class="_end">12-17-2014 12:30:00</span>
+    <span class="_zonecode">6</span>
+    <span class="_summary">Apache Drill - Live Q&amp;A on Twitter</span>
+    <span class="_description">Join us on Twitter for a one-hour, live 
SQL-on-Hadoop Q&amp;A. Use the <strong>hashtag #DrillQA</strong> so the 
panelists can engage with your questions and comments. Apache Drill committers 
Tomer Shiran, Jacques Nadeau, and Ted Dunning, as well as Tableau Product 
Manager Jeff Feng and Data Scientist Dr. Kirk Borne will be on hand to answer 
your questions.</span>
+    <span class="_location">Twitter: #DrillQA</span>
+    <span class="_organizer">Tomer Shiran</span>
+    <span class="_organizer_email"><a 
href="mailto:[email protected]";>[email protected]</a></span>
+    <span class="_all_day_event">false</span>
+    <span class="_date_format">MM-DD-YYYY</span>
+</a></p>
+
+<p>Hadoop has always been a powerful platform, but it is even more so with the 
release of Apache Drill, a valuable technology for self-service data 
exploration on big data. For BI users, this is really exciting news. </p>
+
+<p>With Apache Drill, you can immediately query complex data in native 
formats, such as schema-less data, nested data, and data with rapidly-evolving 
schemas. And with analytic tools likes Tableau, you can easily create queries, 
build dashboards and explore data. </p>
+
+<p>Want to learn how to leverage Apache Drill in order to get better 
analytical insights? </p>
+
+<p><strong>Join us on Twitter</strong> for a one-hour, live SQL-on-Hadoop 
Q&amp;A, next <strong>Wednesday, December 17th starting at 11:30am PST, 2:30pm 
EST</strong>. Use the <strong>hashtag #DrillQA</strong> so the panelists can 
engage with your questions and comments.</p>
+
+<p>Apache Drill committers Tomer Shiran, Jacques Nadeau, and Ted Dunning, as 
well as Tableau Product Manager Jeff Feng and Data Scientist Dr. Kirk Borne 
will be on hand to answer your questions.</p>
+
+<h4 id="tomer-shiran,-apache-drill-founder-(@tshiran)">Tomer Shiran, Apache 
Drill Founder (@tshiran)</h4>
+
+<p>Tomer Shiran is the founder of Apache Drill, and a PMC member and committer 
on the project. He is VP Product Management at MapR, responsible for product 
strategy, roadmap and new feature development. Prior to MapR, Tomer held 
numerous product management and engineering roles at Microsoft, most recently 
as the product manager for Microsoft Internet Security &amp; Acceleration 
Server (now Microsoft Forefront). He is the founder of two websites that have 
served tens of millions of users, and received coverage in prestigious 
publications such as The New York Times, USA Today and The Times of London. 
Tomer is also the author of a 900-page programming book. He holds an MS in 
Computer Engineering from Carnegie Mellon University and a BS in Computer 
Science from Technion - Israel Institute of Technology.</p>
+
+<h4 id="jeff-feng,-product-manager-tableau-software-(@jtfeng)">Jeff Feng, 
Product Manager Tableau Software (@jtfeng)</h4>
+
+<p>Jeff Feng is a Product Manager at Tableau and leads their Big Data product 
roadmap &amp; strategic vision.  In his role, he focuses on joint technology 
integration and partnership efforts with a number of Hadoop, NoSQL and web 
application partners in helping users see and understand their data.</p>
+
+<h4 id="ted-dunning,-apache-drill-comitter-(@ted_dunning)">Ted Dunning, Apache 
Drill Comitter (@Ted_Dunning)</h4>
+
+<p>Ted Dunning is Chief Applications Architect at MapR Technologies and 
committer and PMC member of the Apache Mahout, Apache ZooKeeper, and Apache 
Drill projects and mentor for Apache Storm. He contributed to Mahout 
clustering, classification and matrix decomposition algorithms  and helped 
expand the new version of Mahout Math library. Ted was the chief architect 
behind the MusicMatch (now Yahoo Music) and Veoh recommendation systems, he 
built fraud detection systems for ID Analytics (LifeLock) and he has issued 24 
patents to date. Ted has a PhD in computing science from University of 
Sheffield. When he’s not doing data science, he plays guitar and mandolin.</p>
+
+<h4 id="jacques-nadeau,-vice-president,-apache-drill-(@intjesus)">Jacques 
Nadeau, Vice President, Apache Drill (@intjesus)</h4>
+
+<p>Jacques Nadeau leads Apache Drill development efforts at MapR Technologies. 
He is an industry veteran with over 15 years of big data and analytics 
experience. Most recently, he was cofounder and CTO of search engine startup 
YapMap. Before that, he was director of new product engineering with Quigo 
(contextual advertising, acquired by AOL in 2007). He also built the Avenue A | 
Razorfish analytics data warehousing system and associated services practice 
(acquired by Microsoft).</p>
+
+<h4 id="dr.-kirk-borne,-george-mason-university-(@kirkdborne)">Dr. Kirk Borne, 
George Mason University (@KirkDBorne)</h4>
+
+<p>Dr. Kirk Borne is a Transdisciplinary Data Scientist and an Astrophysicist. 
He is Professor of Astrophysics and Computational Science in the George Mason 
University School of Physics, Astronomy, and Computational Sciences. He has 
been at Mason since 2003, where he teaches and advises students in the graduate 
and undergraduate Computational Science, Informatics, and Data Science 
programs. Previously, he spent nearly 20 years in positions supporting NASA 
projects, including an assignment as NASA&#39;s Data Archive Project Scientist 
for the Hubble Space Telescope, and as Project Manager in NASA&#39;s Space 
Science Data Operations Office. He has extensive experience in big data and 
data science, including expertise in scientific data mining and data systems. 
He has published over 200 articles (research papers, conference papers, and 
book chapters), and given over 200 invited talks at conferences and 
universities worldwide.</p>
+
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+    <div class="int_title">
+      <h1 class="post-title">What's Coming in 2015?</h1>
+    </div>
+    <p class="post-meta">
+    
+      
+      
+      <strong>Author:</strong> Tomer Shiran (Founder, PMC Member and 
Committer, Apache Drill)
+    
+<br/><strong>Date:</strong> Dec 16, 2014
+</p>
+  </header>
+  <div class="addthis_sharing_toolbox"></div>
+
+  <article class="post-content">
+    <p>2014 was an exciting year for the Drill community. In August we made 
Drill available for downloads, and last week the Apache Software Foundation 
promoted Drill to a top-level project. Many of you have asked me what&#39;s 
coming next, so I decided to sit down and outline some of the interesting 
initiatives that the Drill community is currently working on:</p>
+
+<ul>
+<li>Flexible Access Control</li>
+<li>JSON in Any Shape or Form</li>
+<li>Advanced SQL</li>
+<li>New Data Sources</li>
+<li>Drill/Spark Integration</li>
+<li>Operational Enhancements: Speed, Scalability and Workload Management</li>
+</ul>
+
+<p>This is by no means intended to be an exhaustive list of everything that 
will be added to Drill in 2015. With Drill&#39;s rapidly expanding community, I 
anticipate that you&#39;ll see a whole lot more.</p>
+
+<h2 id="flexible-access-control">Flexible Access Control</h2>
+
+<p>Many organizations are now interested in providing Drill as a service to 
their users, supporting many users, groups and organizations with a single 
cluster. To do so, they need to be able to control who can access what data. 
Today&#39;s volume and variety of data requires a new approach to access 
control. For example, it is becoming impractical for organizations to manage a 
standalone, centralized repository of permissions for every column/row of every 
table. Drill&#39;s virtual datasets (views) provide a more scalable solution to 
access control:</p>
+
+<ul>
+<li>The user creates a virtual dataset (<code>CREATE VIEW vd AS SELECT 
...</code>), selecting the data to be exposed/shared. The virtual dataset is 
defined as a SQL statement. For example, a virtual dataset may represent only 
the records that were created in the last 30 days and don&#39;t have the 
<code>restricted</code> flag. It could even mask some columns. Drill&#39;s 
virtual datasets (just the SQL statement) are stored as files in the file 
system, so users can leverage file system permissions to control who can access 
the virtual dataset, without granting access to the source data.</li>
+<li>A virtual dataset is owned by a specific user and can only 
&quot;select&quot; data that the owner has access to. The data sources (HDFS, 
HBase, MongoDB, etc.) are responsible for access control decisions. Users and 
administrators do not need to define separate permissions inside Drill or 
utilize yet another centralized permission repository, such as Sentry and 
Ranger.</li>
+</ul>
+
+<h2 id="json-in-any-shape-or-form">JSON in Any Shape or Form</h2>
+
+<p>When data is <strong>Big</strong> (as in Big Data), it is painful to copy 
and transform it. Users should be able to explore the raw data without (or at 
least prior to) transforming it into another format. Drill is designed to 
enable in-situ analytics. Just point it at a file or directory and run the 
queries.</p>
+
+<p>JSON has emerged as the most common self-describing format, and Drill is 
able to query JSON files out of the box. Drill currently assumes that the JSON 
documents (or records) are stored sequentially in a file:</p>
+<div class="highlight"><pre><code class="language-json" data-lang="json"><span 
class="p">{</span> <span class="nt">&quot;name&quot;</span><span 
class="p">:</span> <span class="s2">&quot;Lee&quot;</span><span 
class="p">,</span> <span class="nt">&quot;yelping_since&quot;</span><span 
class="p">:</span> <span class="s2">&quot;2012-02&quot;</span> <span 
class="p">}</span>
+<span class="p">{</span> <span class="nt">&quot;name&quot;</span><span 
class="p">:</span> <span class="s2">&quot;Matthew&quot;</span><span 
class="p">,</span> <span class="nt">&quot;yelping_since&quot;</span><span 
class="p">:</span> <span class="s2">&quot;2011-12&quot;</span> <span 
class="p">}</span>
+<span class="p">{</span> <span class="nt">&quot;name&quot;</span><span 
class="p">:</span> <span class="s2">&quot;Jasmine&quot;</span><span 
class="p">,</span> <span class="nt">&quot;yelping_since&quot;</span><span 
class="p">:</span> <span class="s2">&quot;2010-09&quot;</span> <span 
class="p">}</span>
+</code></pre></div>
+<p>However, many JSON-based datasets, ranging from <a 
href="http://data.gov";>data.gov</a> (government) datasets to Twitter API 
responses, are not organized as simple sequences of JSON documents. In some 
cases the actual records are listed as elements of an internal array inside a 
single JSON document. For example, consider the following file, which 
technically consists of a single JSON document, but really contains three 
records (under the <code>data.records</code> field):</p>
+<div class="highlight"><pre><code class="language-json" data-lang="json"><span 
class="p">{</span>
+  <span class="nt">&quot;metadata&quot;</span><span class="p">:</span> <span 
class="err">...</span><span class="p">,</span>
+  <span class="nt">&quot;data&quot;</span><span class="p">:</span> <span 
class="p">{</span>
+    <span class="nt">&quot;records&quot;</span><span class="p">:</span> <span 
class="p">[</span>
+      <span class="p">{</span> <span class="nt">&quot;name&quot;</span><span 
class="p">:</span> <span class="s2">&quot;Lee&quot;</span><span 
class="p">,</span> <span class="nt">&quot;yelping_since&quot;</span><span 
class="p">:</span> <span class="s2">&quot;2012-02&quot;</span> <span 
class="p">},</span>
+      <span class="p">{</span> <span class="nt">&quot;name&quot;</span><span 
class="p">:</span> <span class="s2">&quot;Matthew&quot;</span><span 
class="p">,</span> <span class="nt">&quot;yelping_since&quot;</span><span 
class="p">:</span> <span class="s2">&quot;2011-12&quot;</span> <span 
class="p">},</span>
+      <span class="p">{</span> <span class="nt">&quot;name&quot;</span><span 
class="p">:</span> <span class="s2">&quot;Jasmine&quot;</span><span 
class="p">,</span> <span class="nt">&quot;yelping_since&quot;</span><span 
class="p">:</span> <span class="s2">&quot;2010-09&quot;</span> <span 
class="p">}</span>
+    <span class="p">]</span>
+  <span class="p">}</span>
+<span class="p">}</span>
+</code></pre></div>
+<p>The <code>FLATTEN</code> function in Drill 0.7+ takes an array and converts 
each item into a top-level record:</p>
+<div class="highlight"><pre><code class="language-sql" data-lang="sql"><span 
class="k">SELECT</span> <span class="n">FLATTEN</span><span 
class="p">(</span><span class="k">data</span><span class="p">.</span><span 
class="n">records</span><span class="p">)</span> <span class="k">FROM</span> 
<span class="n">dfs</span><span class="p">.</span><span 
class="n">tmp</span><span class="p">.</span><span class="o">`</span><span 
class="n">foo</span><span class="p">.</span><span class="n">json</span><span 
class="o">`</span><span class="p">;</span>
+</code></pre></div>
+<p>You can use this as an inner query (or inside a view):</p>
+<div class="highlight"><pre><code class="language-sql" data-lang="sql"><span 
class="o">&gt;</span> <span class="k">SELECT</span> <span 
class="n">t</span><span class="p">.</span><span class="n">record</span><span 
class="p">.</span><span class="n">name</span> <span class="k">AS</span> <span 
class="n">name</span>
+  <span class="k">FROM</span> <span class="p">(</span><span 
class="k">SELECT</span> <span class="n">FLATTEN</span><span 
class="p">(</span><span class="k">data</span><span class="p">.</span><span 
class="n">records</span><span class="p">)</span> <span class="k">AS</span> 
<span class="n">record</span> <span class="k">FROM</span> <span 
class="n">dfs</span><span class="p">.</span><span class="n">tmp</span><span 
class="p">.</span><span class="o">`</span><span class="n">test</span><span 
class="o">/</span><span class="n">foo</span><span class="p">.</span><span 
class="n">json</span><span class="o">`</span><span class="p">)</span> <span 
class="n">t</span><span class="p">;</span>
+<span class="o">+</span><span class="c1">------------+</span>
+<span class="o">|</span>    <span class="n">name</span>    <span 
class="o">|</span>
+<span class="o">+</span><span class="c1">------------+</span>
+<span class="o">|</span> <span class="n">Lee</span>        <span 
class="o">|</span>
+<span class="o">|</span> <span class="n">Matthew</span>    <span 
class="o">|</span>
+<span class="o">|</span> <span class="n">Jasmine</span>    <span 
class="o">|</span>
+<span class="o">+</span><span class="c1">------------+</span>
+</code></pre></div>
+<p>While this works today, the dataset is technically a single JSON document, 
so Drill ends up reading the entire dataset into memory. We&#39;re developing a 
FLATTEN-pushdown mechanism that will enable the JSON reader to emit the 
individual records into the downstream operators, thereby making this work with 
datasets of arbitrary size. Once that&#39;s implemented, users will be able to 
explore any JSON-based dataset in-situ (ie, without having to transform it).</p>
+
+<h2 id="full-sql">Full SQL</h2>
+
+<p>Unlike the majority of SQL engines for Hadoop and NoSQL databases, which 
support SQL-like languages (HiveQL, CQL, etc.), Drill is designed from the 
ground up to be compliant with ANSI SQL. We simply started with a real SQL 
parser (Apache Calcite, previously known as Optiq). We&#39;re currently 
implementing the remaining SQL constructs, and plan to support the full TPC-DS 
suite (with no query modifications) in 2015. Full SQL support makes BI tools 
work better, and enables users who are proficient with SQL to leverage their 
existing knowledge and skills.</p>
+
+<h2 id="new-data-sources">New Data Sources</h2>
+
+<p>Drill is a standalone, distributed SQL engine. It has a pluggable 
architecture that allows it to support multiple data sources. Drill 0.6 
includes storage plugins for:</p>
+
+<ul>
+<li><a 
href="https://hadoop.apache.org/docs/current/api/org/apache/hadoop/fs/FileSystem.html";>Hadoop
 File System</a> implementations (local file system, HDFS, MapR-FS, Amazon S3, 
etc.)</li>
+<li>HBase and MapR-DB</li>
+<li>MongoDB</li>
+<li>Hive Metastore (query any dataset that is registered in Hive 
Metastore)</li>
+</ul>
+
+<p>A single query can join data from different systems. For example, a query 
can join user profiles in MongoDB with log files in Hadoop, or datasets in 
multiple Hadoop clusters.</p>
+
+<p>I&#39;m eager to see what storage plugins the community develops over the 
next 12 months. In the last few weeks alone, developers in the community have 
expressed their desire (on the <a href="mailto:[email protected]";>public 
list</a>) to develop additional storage plugins for the following data 
sources:</p>
+
+<ul>
+<li>Cassandra</li>
+<li>Solr</li>
+<li>JDBC (any RDBMS, including Oracle, MySQL, PostgreSQL and SQL Server)</li>
+</ul>
+
+<p>If you&#39;re interested in implementing a new storage plugin, I would 
encourage you to reach out to the Drill developer community on <a 
href="mailto:[email protected]";>[email protected]</a>. I&#39;m looking 
forward to publishing an example of a single-query join across 10 data 
sources.</p>
+
+<h2 id="drill/spark-integration">Drill/Spark Integration</h2>
+
+<p>We&#39;re seeing growing interest in Spark as an execution engine for data 
pipelines, providing an alternative to MapReduce. The Drill community is 
working on integrating Drill and Spark to address a few new use cases:</p>
+
+<ul>
+<li><p>Use a Drill query (or view) as the input to Spark. Drill is a powerful 
engine for extracting and pre-processing data from various data sources, 
thereby reducing development time and effort. Here&#39;s an example:</p>
+<div class="highlight"><pre><code class="language-scala" 
data-lang="scala"><span class="k">val</span> <span class="n">sc</span> <span 
class="k">=</span> <span class="k">new</span> <span 
class="nc">SparkContext</span><span class="o">(</span><span 
class="n">conf</span><span class="o">)</span>
+<span class="k">val</span> <span class="n">result</span> <span 
class="k">=</span> <span class="n">sc</span><span class="o">.</span><span 
class="n">drillRDD</span><span class="o">(</span><span class="s">&quot;SELECT * 
FROM dfs.root.`path/to/logs` l, mongo.mydb.users u WHERE l.user_id = u.id GROUP 
BY ...&quot;</span><span class="o">)</span>
+<span class="k">val</span> <span class="n">formatted</span> <span 
class="k">=</span> <span class="n">result</span><span class="o">.</span><span 
class="n">map</span> <span class="o">{</span> <span class="n">r</span> <span 
class="k">=&gt;</span>
+  <span class="k">val</span> <span class="o">(</span><span 
class="n">first</span><span class="o">,</span> <span class="n">last</span><span 
class="o">,</span> <span class="n">visits</span><span class="o">)</span> <span 
class="k">=</span> <span class="o">(</span><span class="n">r</span><span 
class="o">.</span><span class="n">name</span><span class="o">.</span><span 
class="n">first</span><span class="o">,</span> <span class="n">r</span><span 
class="o">.</span><span class="n">name</span><span class="o">.</span><span 
class="n">last</span><span class="o">,</span> <span class="n">r</span><span 
class="o">.</span><span class="n">visits</span><span class="o">)</span>
+  <span class="n">s</span><span class="s">&quot;$first $last 
$visits&quot;</span>
+<span class="o">}</span>
+</code></pre></div></li>
+<li><p>Use Drill to query Spark RDDs. Analysts will be able to use BI tools 
like MicroStrategy, Spotfire and Tableau to query in-memory data in Spark. In 
addition, Spark developers will be able to embed Drill execution in a Spark 
data pipeline, thereby enjoying the power of Drill&#39;s schema-free, columnar 
execution engine.</p></li>
+</ul>
+
+<h2 id="operational-enhancements">Operational Enhancements</h2>
+
+<p>As we continue with our monthly releases and march towards the 1.0 release 
early next year, we&#39;re focused on improving Drill&#39;s speed and 
scalability. We&#39;ll also enhance Drill&#39;s multi-tenancy with more 
advanced workload management.</p>
+
+<ul>
+<li><strong>Speed</strong>: Drill is already extremely fast, and we&#39;re 
going to make it even faster over the next few months. With that said, we think 
that improving user productivity and time-to-insight is as important as shaving 
a few milliseconds off a query&#39;s runtime.</li>
+<li><strong>Scalability</strong>: To date we&#39;ve focused mainly on clusters 
of up to a couple hundred nodes. We&#39;re currently working to support 
clusters with thousands of nodes. We&#39;re also improving concurrency to 
better support deployments in which hundreds of analysts or developers are 
running queries at the same time.</li>
+<li><strong>Workload management</strong>: A single cluster is often shared 
among many users and groups, and everyone expects answers in real-time. 
Workload management prioritizes the allocation of resources to ensure that the 
most important workloads get done first so that business demands can be met. 
Administrators need to be able to assign priorities and quotas at a fine 
granularity. We&#39;re working on enhancing Drill&#39;s workload management to 
provide these capabilities while providing tight integration with YARN and 
Mesos.</li>
+</ul>
+
+<h2 id="we-would-love-to-hear-from-you!">We Would Love to Hear From You!</h2>
+
+<p>Are there other features you would like to see in Drill? We would love to 
hear from you:</p>
+
+<ul>
+<li>Drill users: <a 
href="mailto:[email protected]";>[email protected]</a></li>
+<li>Drill developers: <a 
href="mailto:[email protected]";>[email protected]</a></li>
+<li>Me: <a href="mailto:[email protected]";>[email protected]</a></li>
+</ul>
+
+<p>Happy Drilling!<br>
+Tomer Shiran</p>
+
+  </article>
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+  <header class="post-header">
+    <div class="int_title">
+      <h1 class="post-title">Drill 0.7 Released</h1>
+    </div>
+    <p class="post-meta">
+    
+      
+      
+      <strong>Author:</strong> Jacques Nadeau (PMC Chair and Committer, Apache 
Drill)
+    
+<br/><strong>Date:</strong> Dec 23, 2014
+</p>
+  </header>
+  <div class="addthis_sharing_toolbox"></div>
+
+  <article class="post-content">
+    <p>I&#39;m excited to announce that the community has just released Drill 
0.7, which includes <a 
href="https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12313820&amp;version=12327473";>228
 resolved JIRAs</a> and numerous enhancements such as: </p>
+
+<ul>
+<li>No dependency on UDP multicast. Drill can now work on EC2, as well as 
clusters with multiple subnets or multihomed configurations</li>
+<li><a 
href="https://cwiki.apache.org/confluence/display/DRILL/Partition+Pruning";>Automatic
 partition pruning</a> based on directory structures</li>
+<li>New nested data functions: <a 
href="https://cwiki.apache.org/confluence/display/DRILL/KVGEN+Function";>KVGEN</a>
 and <a 
href="https://cwiki.apache.org/confluence/display/DRILL/FLATTEN+Function";>FLATTEN</a></li>
+<li>Fast &quot;schema&quot; return. This provides a better experience when 
using BI tools</li>
+<li>Hive 0.13 Metastore support</li>
+<li>Improved performance for queries on JSON data</li>
+</ul>
+
+<p>You can now <a href="/download/">download Drill 0.7</a>. As always, you may 
check out the official <a 
href="https://cwiki.apache.org/confluence/display/DRILL/Release+Notes";>release 
notes</a> for more details.</p>
+
+<p>In case you&#39;re interested in understanding more about where we&#39;re 
heading, check out Tomer&#39;s recent blog post outlining some of the <a 
href="/blog/2014/12/16/whats-coming-in-2015/">planned initiatives for 
2015</a>.</p>
+
+<p>Happy Drilling!<br>
+Jacques Nadeau</p>
+
+  </article>
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+<html>
+
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+    <div class="int_title">
+      <h1 class="post-title">Schema-free JSON Data Infrastructure</h1>
+    </div>
+    <p class="post-meta">
+    
+      
+      
+      <strong>Author:</strong> Tomer Shiran (Founder, PMC Member and 
Committer, Apache Drill)
+    
+<br/><strong>Date:</strong> Jan 27, 2015
+</p>
+  </header>
+  <div class="addthis_sharing_toolbox"></div>
+
+  <article class="post-content">
+    <p>JSON has emerged in recent years as the de-facto standard data exchange 
format. It is being used everywhere. Front-end Web applications use JSON to 
maintain data and communicate with back-end applications. Web APIs are 
JSON-based (eg, <a href="https://dev.twitter.com/rest/public";>Twitter REST 
APIs</a>, <a href="http://developers.marketo.com/documentation/rest/";>Marketo 
REST APIs</a>, <a href="https://developer.github.com/v3/";>GitHub API</a>). 
It&#39;s the format of choice for public datasets, operational log files and 
more.</p>
+
+<h1 id="why-is-json-a-convenient-data-exchange-format?">Why is JSON a 
Convenient Data Exchange Format?</h1>
+
+<p>While I won&#39;t dive into the historical roots of JSON (JavaScript Object 
Notation, <a 
href="http://en.wikipedia.org/wiki/JSON#JavaScript_eval.28.29";><code>eval()</code></a>,
 etc.), I do want to highlight several attributes of JSON that make it a 
convenient data exchange format:</p>
+
+<ul>
+<li><strong>JSON is self-describing</strong>. You can look at a JSON document 
and understand what it represents. The field names are included in the 
document. You don&#39;t need an external schema or definition to interpret 
JSON-encoded data. This makes life easier for anyone who wants to deal with the 
data, and it also means that a collection of JSON documents represents what 
many people call a &quot;schema-less dataset&quot; (where structure can evolve, 
and different records can have different fields).</li>
+<li><strong>JSON is simple</strong>. Other self-describing formats such as XML 
are much more complicated. A JSON document is made up of arrays and maps (or 
objects, in JSON terminology), and that&#39;s about it.</li>
+<li><strong>JSON can naturally represent real-world objects</strong>. Try 
representing your application&#39;s <code>Customer</code> object (with the 
person&#39;s address, order history, etc.) in a CSV file or a relational 
database. It&#39;s hard. In fact, ORM systems were invented to help alleviate 
this issue.</li>
+<li><strong>JSON libraries are available in virtually every programming 
language</strong>. Take a look at <a href="http://www.json.org/";>the list of 
supported languages on JSON.org</a>. I counted 15 languages that start with the 
letters A, B or C.</li>
+<li><strong>JSON is idiomatic in loosely typed languages</strong>. Many 
loosely typed languages, such as Python, Ruby and JavaScript, have data 
structures that are similar to JSON objects, making it very natural to handle 
JSON data in those languages. For example, a Python dictionary looks just like 
a JSON object. This makes it easy for developers to utilize JSON in their 
applications.</li>
+</ul>
+
+<h1 id="json-data-infrastructure">JSON Data Infrastructure</h1>
+
+<p>Traditional data infrastructure, such as relational databases, has some 
features that make it easier to store and process JSON-encoded data. For 
example, Oracle has <a 
href="https://docs.oracle.com/database/121/ADXDB/json.htm";>a JSON data type and 
a set of functions for handling JSON data</a>.</p>
+
+<p>However, a new class of data infrastructure is providing a much more 
seamless experience via a full-fledged JSON data model. For example:</p>
+
+<ul>
+<li>Drill is a SQL engine in which each record is conceptually a JSON 
document.</li>
+<li>Elasticsearch is a search engine in which each indexed document is 
conceptually a JSON document.</li>
+<li>MongoDB is an operational database in which each record is conceptually a 
JSON document.</li>
+</ul>
+
+<p>These systems view JSON as a data model as opposed to one of many data 
types, realizing that JSON offers a simple way to represent real-world 
objects.</p>
+
+<table><thead>
+<tr>
+<th></th>
+<th>Traditional Infrastructure</th>
+<th>JSON Infrastructure</th>
+</tr>
+</thead><tbody>
+<tr>
+<td><strong>Examples:</strong></td>
+<td>Oracle, SQL Server</td>
+<td>Drill, Elasticsearch, MongoDB</td>
+</tr>
+<tr>
+<td><strong>Record:</strong></td>
+<td>Tuple</td>
+<td>JSON document</td>
+</tr>
+<tr>
+<td><strong>Variable schema:</strong></td>
+<td>No</td>
+<td>Yes</td>
+</tr>
+</tbody></table>
+
+<p>If you happen to be in the Bay Area tomorrow, please join Gaurav Gupta (VP 
Product Management, Elasticsearch), Paul Pedersen (Deputy CTO, MongoDB), Robert 
Greene (Senior Principal Product Manager, Oracle), Sukanta Ganguly (VP 
Solutions Architecture, Aerospike) and me for a panel moderated by 
Gartner&#39;s Nick Heudecker on this new world of schema-free JSON. Check out 
<a href="http://www.meetup.com/SF-Bay-Areas-Big-Data-Think-Tank/";>The Hive Big 
Data Think Tank</a> for more information.</p>
+
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http://git-wip-us.apache.org/repos/asf/drill-site/blob/c4de0f83/blog/2015/03/23/microstrategy-announces-drill-support/index.html
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+      <h1 class="post-title">MicroStrategy Announces Drill Support</h1>
+    </div>
+    <p class="post-meta">
+    
+      
+      
+      <strong>Author:</strong> Tomer Shiran (Founder, PMC Member and 
Committer, Apache Drill)
+    
+<br/><strong>Date:</strong> Mar 23, 2015
+</p>
+  </header>
+  <div class="addthis_sharing_toolbox"></div>
+
+  <article class="post-content">
+    <p>Today MicroStrategy  <a 
href="http://ir.microstrategy.com/releasedetail.cfm?ReleaseID=902795";>announced</a>
 that it has certified its platform with Apache Drill. According to 
MicroStrategy&#39;s CTO, Tim Lang, Drill reduces the time-to-value for 
MicroStrategy users, and enables them to leverage multi-structured data.</p>
+
+<p>Many early adopters of Drill have been interested in leveraging 
MicroStrategy&#39;s powerful BI platform. With it&#39;s first-class support for 
self-describing data and evolving structure, Drill enables MicroStrategy users 
to explore and analyze the data in Hadoop and NoSQL databases without the usual 
friction that comes with having to define and manage schemas.</p>
+
+<p>If you would like to learn more about this integration, <a 
href="http://info.microstrategy.com/accessing-multi-structured-data-sources";>sign
 up</a> for MicroStrategy&#39;s webinar next month, which includes a live demo 
of the integration with Drill.</p>
+
+<p>Happy Drilling!<br>
+Tomer Shiran</p>
+
+  </article>
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http://git-wip-us.apache.org/repos/asf/drill-site/blob/c4de0f83/blog/2015/03/31/drill-0.8-released/index.html
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+<html>
+
+<head>
+
+<meta charset="UTF-8">
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+    <div class="int_title">
+      <h1 class="post-title">Drill 0.8 Released</h1>
+    </div>
+    <p class="post-meta">
+    
+    <strong>Authors:</strong>
+    <ul>
+      
+        
+        <li>Tomer Shiran (Founder, PMC Member and Committer, Apache Drill)</li>
+      
+        
+        <li>Jacques Nadeau (PMC Chair and Committer, Apache Drill)</li>
+      
+    </ul>
+    
+<br/><strong>Date:</strong> Mar 31, 2015
+</p>
+  </header>
+  <div class="addthis_sharing_toolbox"></div>
+
+  <article class="post-content">
+    <p>We&#39;re excited to announce that the community has just released 
Drill 0.8, which includes <a 
href="https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12313820&amp;version=12328812";>243
 resolved JIRAs</a> and numerous enhancements such as: </p>
+
+<ul>
+<li><strong>Bytecode rewriting</strong>. Drill now leverages code optimization 
techniques such as bytecode rewriting and inlining to enhance the speed of many 
queries by reducing overall memory usage and CPU instructions.</li>
+<li><strong>Advanced partition pruning</strong>. Drill can now prune 
partitions based on arbitrarily complex expressions. For example, specify 
<code>WHERE dir0 LIKE &#39;2015-%&#39;</code> and your query will look inside 
the directory &quot;2015-01&quot; but not inside &quot;2014-12&quot;.</li>
+<li><strong>Real-time query diagnostics</strong>. You can now see exactly what 
your queries are doing in real-time, making it easy to troubleshoot, optimize 
and manage execution.</li>
+<li><strong>Large records, large # of files</strong>. Drill was previously 
limited to small records of up to 128KB. It now supports records of any size. 
In addition, query performance has been improved when dealing with large 
numbers of files thanks to a variety of optimizations such as parallel metadata 
reads.</li>
+<li><strong>More SQL</strong>. Drill now features complete support for 
<code>UNION ALL</code> and <code>COUNT(DISTINCT)</code>. Drill 0.8 also 
includes new functions such as <code>unix_timestamp</code> and the window 
functions <code>sum</code>, <code>count</code> and <code>rank</code>. Note that 
these window functions should be considered beta.</li>
+<li><strong>Better compression</strong>. Drill can now query compressed JSON 
files. In addition, the user can control Parquet compression in CTAS 
(<code>CREATE TABLE AS</code>) statements.</li>
+<li><strong>Performance</strong>. Drill 0.8 includes broadcast joins, 
disk-based joins, parallel metadata reads and many other performance-related 
enhancements.</li>
+<li><strong>Reliability</strong>. Drill 0.8 includes a variety of fixes that 
improve the stability of the drillbit daemon, the sqlline shell and the ODBC 
and JDBC drivers.</li>
+<li><strong>HBase 0.98 support</strong>. You can now run SQL queries on any 
HBase 0.98 table.</li>
+</ul>
+
+<p>You can now <a href="/download/">download Drill 0.8</a>. As always, you may 
check out the official <a 
href="https://cwiki.apache.org/confluence/display/DRILL/Release+Notes";>release 
notes</a> for more details.</p>
+
+<p>Happy Drilling!<br>
+Tomer Shiran and Jacques Nadeau</p>
+
+  </article>
+ <div id="disqus_thread"></div>
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