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Posted to commits@storm.apache.org by sr...@apache.org on 2015/09/29 05:20:03 UTC
[02/15] storm git commit: add people section,
clean up videos/slides section
http://git-wip-us.apache.org/repos/asf/storm/blob/fde3433f/_site/talksAndVideos.html
----------------------------------------------------------------------
diff --git a/_site/talksAndVideos.html b/_site/talksAndVideos.html
index 05781eb..f772d28 100644
--- a/_site/talksAndVideos.html
+++ b/_site/talksAndVideos.html
@@ -67,9 +67,10 @@
<li><a href="/documentation.html" id="documentation">Documentation</a></li>
<li><a href="/talksAndVideos.html">Talks and Slideshows</a></li>
<li class="dropdown">
- <a href="#" class="dropdown-toggle" data-toggle="dropdown" id="contribute">Contribute <b class="caret"></b></a>
+ <a href="#" class="dropdown-toggle" data-toggle="dropdown" id="contribute">Community <b class="caret"></b></a>
<ul class="dropdown-menu">
- <li><a href="/contribute/Contributing-to-Storm.html">Getting Started</a></li>
+ <li><a href="/contribute/Contributing-to-Storm.html">Contributing</a></li>
+ <li><a href="/contribute/People.html">People</a></li>
<li><a href="/contribute/BYLAWS.html">ByLaws</a></li>
</ul>
</li>
@@ -99,178 +100,216 @@
<div class="tab-content">
<div role="tabpanel" class="tab-pane active" id="talks">
+
+
+
+
+<!-- ################### -->
<div class="brickSS">
<div class="row">
<div class="col-md-6">
- <iframe width="560" height="315" src="https://www.youtube.com/embed/LpNbjXFPyZ0" frameborder="0" allowfullscreen></iframe>
- </div>
- <div class="col-md-6">
- <h3>Storm: distributed and fault-tolerant realtime computation from
- <span>
- nathanmarz
- </span>
- </h3>
+ <h3>Andrew Montalenti - streamparse: real-time streams with Python and Apache Storm - PyCon 2015</h3>
<div>
- <p>Published on Nov 29, 2013
- This is a technical architect's case study of how Loggly has employed the latest social-media-scale technologies as the backbone ingestion processing for our multi-tenant, geo-distributed, and real-time log management system. This presentation describes design details of how we built a second-generation system fully leveraging AWS services including Amazon Route 53 DNS with heartbeat and latency-based routing, multi-region VPCs, Elastic Load Balancing, Amazon Relational Database Service, and a number of pro-active and re-active approaches to scaling computational and indexing capacity.
- The talk includes lessons learned in our first generation release, validated by thousands of customers; speed bumps and the mistakes we made along the way; various data models and architectures previously considered; and success at scale: speeds, feeds, and an unmeltable log processing engine.</p>
+ <p>Published on Apr 12, 2015</p><p>
+ Real-time streams are everywhere, but does Python have a good way of processing them? Until recently, there were no good options. A new open source project, streamparse, makes working with real-time data streams easy for Pythonistas. If you have ever wondered how to process 10,000 data tuples per second with Python -- while maintaining high availability and low latency -- this talk is for you.</p>
</div>
</div>
+ <div class="col-md-6">
+ <iframe width="560" height="315" src="https://www.youtube.com/embed/l1MM_SHDrPY" frameborder="0" allowfullscreen></iframe>
+ </div>
</div>
</div>
+<!-- ################### -->
+
<div class="brickSS">
<div class="row">
<div class="col-md-6">
- <h3>Apache Storm for Real-Time Processing in Hadoop
- <span>
- Hortonworks
- </span>
+ <iframe width="560" height="315" src="https://www.youtube.com/embed/hVO5nbxnBkU" frameborder="0" allowfullscreen></iframe>
+ </div>
+ <div class="col-md-6">
+ <h3>Real-Time Big Data Analytics with Storm
</h3>
<div>
- <p>Published on Jan 29, 2014
- In this video, we cover:
-
- -- 5 key benefits of Apache Storm
- -- The reasons for adding Apache Storm to Hortonworks Data Platform
- -- How Apache Hadoop YARN opened the door for integration of Storm into Hadoop
- -- Two general use case patterns with Storm and specific uses in transportation and advertising
- -- How Ambari will provide a single view and common operational platform for enterprises to distribute cluster resources across different workloads</p>
+ <p>Published on Oct 12, 2013</p><p>
+ This talk provides an overview of the open source Storm system for processing Big Data in realtime. The talk starts with an overview of the technology, including key components: Nimbus, Zookeeper, Topology, Tuple, Trident. The presentation then dives into the complex Big Data architecture in which Storm can be integrated. The result is a compelling stack of technologies including integrated Hadoop clusters, MPP, and NoSQL databases.
+
+ The presentation then reviews real world use cases for realtime Big Data analytics. Social updates, in particular real-time news feeds on sites like Twitter and Facebook, benefit from Storm's capacity to process benefits from distributed logic of streaming. Another case study is financial compliance monitoring, where Storm plays a primary role in reducing the market data to a useable subset in realtime. In a final use case, Storm is crucial to collect rich operational intelligence, because it builds multidimensional stats and executes distributed queries.</p>
</div>
</div>
- <div class="col-md-6">
- <iframe width="560" height="315" src="https://www.youtube.com/embed/l1MM_SHDrPY" frameborder="0" allowfullscreen></iframe>
- </div>
</div>
</div>
+<!-- ################### -->
+
+
<div class="brickSS">
<div class="row">
<div class="col-md-6">
- <iframe width="560" height="315" src="https://www.youtube.com/embed/hVO5nbxnBkU" frameborder="0" allowfullscreen></iframe>
+ <iframe width="560" height="315" src="https://www.youtube.com/embed/od8U-XijzlQ" frameborder="0" allowfullscreen></iframe>
</div>
<div class="col-md-6">
- <h3>Real-Time Big Data Analytics with Storm
- <span>
- Aerospike
- </span>
+ <h3>Andrew Montalenti & Keith Bourgoin - Real-time streams and logs with Storm and Kafka - PyData SV 2014
</h3>
<div>
- <p>Published on Oct 12, 2013
- This talk provides an overview of the open source Storm system for processing Big Data in realtime. The talk starts with an overview of the technology, including key components: Nimbus, Zookeeper, Topology, Tuple, Trident. The presentation then dives into the complex Big Data architecture in which Storm can be integrated. The result is a compelling stack of technologies including integrated Hadoop clusters, MPP, and NoSQL databases.
+ <p>Published on Jun 12, 2014</p><p>
+
+ Some of the biggest issues at the center of analyzing large amounts of data are query flexibility, latency, and fault tolerance. Modern technologies that build upon the success of "big data" platforms, such as Apache Hadoop, have made it possible to spread the load of data analysis to commodity machines, but these analyses can still take hours to run and do not respond well to rapidly-changing data sets.</p>
- The presentation then reviews real world use cases for realtime Big Data analytics. Social updates, in particular real-time news feeds on sites like Twitter and Facebook, benefit from Storm's capacity to process benefits from distributed logic of streaming. Another case study is financial compliance monitoring, where Storm plays a primary role in reducing the market data to a useable subset in realtime. In a final use case, Storm is crucial to collect rich operational intelligence, because it builds multidimensional stats and executes distributed queries.</p>
+ <p>A new generation of data processing platforms -- which we call "stream architectures" -- have converted data sources into streams of data that can be processed and analyzed in real-time. This has led to the development of various distributed real-time computation frameworks (e.g. Apache Storm) and multi-consumer data integration technologies (e.g. Apache Kafka). Together, they offer a way to do predictable computation on real-time data streams.</p>
+
+ <p>In this talk, we will give an overview of these technologies and how they fit into the Python ecosystem. This will include a discussion of current open source interoperability options with Python, and how to combine real-time computation with batch logic written for Hadoop. We will also discuss Kafka and Storm's alternatives, current industry usage, and some real-world examples of how these technologies are being used in production by Parse.ly today.</p>
</div>
</div>
</div>
</div>
-
+<!-- ################### -->
<div class="brickSS">
<div class="row">
<div class="col-md-6">
- <h3>ETE 2012 - Nathan Marz on Storm
+ <h3>Yahoo talks about Spark vs. Storm
<span>
- ChariotSolutions
+ Jim Scott
</span>
</h3>
<div>
- <p>Published on May 15, 2012
- "Storm makes it easy to write and scale complex realtime computations on a cluster of computers, doing for realtime processing what Hadoop did for batch processing. Storm guarantees that every message will be processed. And it's fast -- you can process millions of messages per second with a small cluster. Best of all, you can write Storm topologies using any programming language. Storm was open-sourced by Twitter in September of 2011 and has since been adopted by numerous companies around the world.
- Storm provides a small set of simple, easy to understand primitives. These primitives can be used to solve a stunning number of realtime computation problems, from stream processing to continuous computation to distributed RPC. In this talk you'll learn:
+ <p>Published on Sep 18, 2014</p><p>
+ Bobby Evans and Tom Graves, the engineering leads for Spark and Storm development at Yahoo will talk about how these technologies are used on Yahoo's grids and reasons why to use one or the other.</p>
- - The concepts of Storm: streams, spouts, bolts, and topologies
- - Developing and testing topologies using Storm's local mode
- - Deploying topologies on Storm clusters
- - How Storm achieves fault-tolerance and guarantees data processing
- - Computing intense functions on the fly in parallel using Distributed RPC
- - Making realtime computations idempotent using transactional topologies
- - Examples of production usage of Storm</p>
+ <p>Bobby Evans is the low latency data processing architect at Yahoo. He is a PMC member on many Apache projects including Storm, Hadoop, Spark, and Tez. His team is responsible for delivering Storm as a service to all of Yahoo and maintaining Spark on Yarn for Yahoo (Although Tom really does most of that work).</p>
+
+ <p>Tom Graves a Senior Software Engineer on the Platform team at Yahoo. He is an Apache PMC member on Hadoop, Spark, and Tez. His team is responsible for delivering and maintaining Spark on Yarn for Yahoo.</p>
</div>
</div>
<div class="col-md-6">
- <iframe width="420" height="315" src="https://www.youtube.com/embed/bdps8tE0gYo" frameborder="0" allowfullscreen></iframe>
+ <iframe width="560" height="315" src="https://www.youtube.com/embed/uJ5rdAPHE1w" frameborder="0" allowfullscreen></iframe>
</div>
+
</div>
</div>
-
+<!-- ################### -->
<div class="brickSS">
<div class="row">
<div class="col-md-6">
- <iframe width="560" height="315" src="https://www.youtube.com/embed/od8U-XijzlQ" frameborder="0" allowfullscreen></iframe>
+ <iframe width="560" height="315" src="https://www.youtube.com/embed/5F0eQ7mkpTU" frameborder="0" allowfullscreen></iframe>
</div>
<div class="col-md-6">
- <h3>Andrew Montalenti & Keith Bourgoin - Real-time streams and logs with Storm and Kafka
+ <h3>Apache Storm Deployment and Use Cases by Spotify Developers
<span>
- PyData
+ Hakka Labs
</span>
</h3>
<div>
- <p>Published on Jun 12, 2014
- PyData SV 2014
- Some of the biggest issues at the center of analyzing large amounts of data are query flexibility, latency, and fault tolerance. Modern technologies that build upon the success of "big data" platforms, such as Apache Hadoop, have made it possible to spread the load of data analysis to commodity machines, but these analyses can still take hours to run and do not respond well to rapidly-changing data sets.
+ <p>Published on Apr 3, 2014</p><p>
+ This talk was presented at the New York City Storm User Group hosted by Spotify on March 25, 2014.</p><p>
+ This is the first time that a Spotify engineer has spoken publicly about their deployment and use cases for Storm! In this talk, Software Engineer Neville Li describes:
+ <ul><li>
+ Real-time features developed using Storm and Kafka including recommendations, social features, data visualization and ad targeting</li>
- A new generation of data processing platforms -- which we call "stream architectures" -- have converted data sources into streams of data that can be processed and analyzed in real-time. This has led to the development of various distributed real-time computation frameworks (e.g. Apache Storm) and multi-consumer data integration technologies (e.g. Apache Kafka). Together, they offer a way to do predictable computation on real-time data streams.
+ <li>Architecture</li>
- In this talk, we will give an overview of these technologies and how they fit into the Python ecosystem. This will include a discussion of current open source interoperability options with Python, and how to combine real-time computation with batch logic written for Hadoop. We will also discuss Kafka and Storm's alternatives, current industry usage, and some real-world examples of how these technologies are being used in production by Parse.ly today.</p>
+ <li>Production integration</li>
+
+ <li>Best practices for deployment</li></ul></p>
+ <p>Spotify is an exciting case study - users create 600 Gigabyte of data per day and 150 Gigabyte of data per day via different services. Every day 4 Terabyte of data is generated in Hadoop, a 700-node cluster running over 2.000 jobs per day. They currently have 28 Petabytes of storage, spread out over 4 data centres across the world.</p>
</div>
</div>
</div>
</div>
-
+
+<!-- ################### -->
+<!-- ################### -->
<div class="brickSS">
<div class="row">
<div class="col-md-6">
- <h3>Yahoo talks about Spark vs. Storm
- <span>
- Jim Scott
- </span>
- </h3>
+ <h3>Nathan Bijnens: A Real-Time Architecture Using Hadoop & Storm</h3>
<div>
- <p>Published on Sep 18, 2014
- Bobby Evans and Tom Graves, the engineering leads for Spark and Storm development at Yahoo will talk about how these technologies are used on Yahoo's grids and reasons why to use one or the other.
-
- Bobby Evans is the low latency data processing architect at Yahoo. He is a PMC member on many Apache projects including Storm, Hadoop, Spark, and Tez. His team is responsible for delivering Storm as a service to all of Yahoo and maintaining Spark on Yarn for Yahoo (Although Tom really does most of that work).
+ <p>Published on Dec 19, 2013</p>
+ <p>With the proliferation of data sources and growing user bases, the amount of data generated requires new ways for storage and processing. Hadoop opened new possibilities, yet it falls short of instant delivery. Adding stream processing using Nathan Marz's Storm, can overcome this delay and bridge the gap to real-time aggregation and reporting. On the Batch layer all master data is kept and is immutable. Once the base data is stored a recurring process will index the data. This process reads all master data, parses it and will create new views out of it. The new views will replace all previously created views. In the Speed layer data is stored not yet absorbed in the Batch layer. Hours of data instead of years of data. Once the data is indexed in the Batch layer the data can discarded in the Speed layer. The Query Service merges the data from the Speed and Batch layers. This talk focuses on the Lambda architecture, which combines multiple technologi
es to be able to process vast amounts of data, while still being able to react timely and report near real-time statistics. Filmed at JAX London 2013.</p>
+ </div>
+ </div>
+ <div class="col-md-6">
+ <iframe width="560" height="315" src="https://www.youtube.com/embed/CrABmVi12_A" frameborder="0" allowfullscreen></iframe>
+ </div>
+
+ </div>
+ </div>
+<!-- ################### -->
- Tom Graves a Senior Software Engineer on the Platform team at Yahoo. He is an Apache PMC member on Hadoop, Spark, and Tez. His team is responsible for delivering and maintaining Spark on Yarn for Yahoo.</p>
+ <div class="brickSS">
+ <div class="row">
+ <div class="col-md-6">
+ <h3>Infrastructure at Scale: Apache Kafka, Twitter Storm & Elastic Search</h3>
+ <div>
+ <p>Published on Nov 29, 2013</p><p>
+ This is a technical architect's case study of how Loggly has employed the latest social-media-scale technologies as the backbone ingestion processing for our multi-tenant, geo-distributed, and real-time log management system. This presentation describes design details of how we built a second-generation system fully leveraging AWS services including Amazon Route 53 DNS with heartbeat and latency-based routing, multi-region VPCs, Elastic Load Balancing, Amazon Relational Database Service, and a number of pro-active and re-active approaches to scaling computational and indexing capacity.</p>
+ <p>
+ The talk includes lessons learned in our first generation release, validated by thousands of customers; speed bumps and the mistakes we made along the way; various data models and architectures previously considered; and success at scale: speeds, feeds, and an unmeltable log processing engine.</p>
</div>
</div>
<div class="col-md-6">
- <iframe width="560" height="315" src="https://www.youtube.com/embed/uJ5rdAPHE1w" frameborder="0" allowfullscreen></iframe>
+ <iframe width="560" height="315" src="https://www.youtube.com/embed/LpNbjXFPyZ0" frameborder="0" allowfullscreen></iframe>
</div>
</div>
</div>
+<!-- ################### -->
+
+<!-- ################### -->
<div class="brickSS">
<div class="row">
<div class="col-md-6">
- <iframe width="560" height="315" src="https://www.youtube.com/embed/5F0eQ7mkpTU" frameborder="0" allowfullscreen></iframe>
+ <iframe width="560" height="315" src="https://www.youtube.com/embed/hVO5nbxnBkU" frameborder="0" allowfullscreen></iframe>
</div>
<div class="col-md-6">
- <h3>Apache Storm Deployment and Use Cases by Spotify Developers
- <span>
- Hakka Labs
- </span>
+ <h3>Real-Time Big Data Analytics with Storm
</h3>
<div>
- <p>Published on Apr 3, 2014
- This talk was presented at the New York City Storm User Group hosted by Spotify on March 25, 2014. More info here: http://www.hakkalabs.co/articles/stor...
- This is the first time that a Spotify engineer has spoken publicly about their deployment and use cases for Storm! In this talk, Software Engineer Neville Li describes:
-
- Real-time features developed using Storm and Kafka including recommendations, social features, data visualization and ad targeting
-
- Architecture
+ <p>Published on Oct 12, 2013</p><p>
+ This talk provides an overview of the open source Storm system for processing Big Data in realtime. The talk starts with an overview of the technology, including key components: Nimbus, Zookeeper, Topology, Tuple, Trident. The presentation then dives into the complex Big Data architecture in which Storm can be integrated. The result is a compelling stack of technologies including integrated Hadoop clusters, MPP, and NoSQL databases.</p>
+ <p>
+ The presentation then reviews real world use cases for realtime Big Data analytics. Social updates, in particular real-time news feeds on sites like Twitter and Facebook, benefit from Storm's capacity to process benefits from distributed logic of streaming. Another case study is financial compliance monitoring, where Storm plays a primary role in reducing the market data to a useable subset in realtime. In a final use case, Storm is crucial to collect rich operational intelligence, because it builds multidimensional stats and executes distributed queries.</p>
+ </div>
+ </div>
+ </div>
+ </div>
- Production integration
+<!-- ################### -->
- Best practices for deployment</p>
+
+<!-- ################### -->
+ <div class="brickSS">
+ <div class="row">
+ <div class="col-md-6">
+ <h3>ETE 2012: Nathan Marz on Storm
+ </h3>
+ <div>
+ <p>Published on May 15, 2012</p><p>
+
+ Storm makes it easy to write and scale complex realtime computations on a cluster of computers, doing for realtime processing what Hadoop did for batch processing. Storm guarantees that every message will be processed. And it's fast -- you can process millions of messages per second with a small cluster. Best of all, you can write Storm topologies using any programming language. Storm was open-sourced by Twitter in September of 2011 and has since been adopted by numerous companies around the world.</p><p>
+ Storm provides a small set of simple, easy to understand primitives. These primitives can be used to solve a stunning number of realtime computation problems, from stream processing to continuous computation to distributed RPC. In this talk you'll learn:
+ <ul>
+ <li>The concepts of Storm: streams, spouts, bolts, and topologies</li>
+ <li>Developing and testing topologies using Storm's local mode</li>
+ <li>Deploying topologies on Storm clusters</li>
+ <li>How Storm achieves fault-tolerance and guarantees data processing</li>
+ <li>Computing intense functions on the fly in parallel using Distributed RPC</li>
+ <li>Making realtime computations idempotent using transactional topologies</li>
+ <li>Examples of production usage of Storm</li></ul></p>
</div>
</div>
+ <div class="col-md-6">
+ <iframe width="560" height="315" src="https://www.youtube.com/embed/bdps8tE0gYo" frameborder="0" allowfullscreen></iframe>
+ </div>
+
</div>
</div>
-
+<!-- ################### -->
+<!-- ########## END VIDEOS ######### -->
</div>
+
<div role="tabpanel" class="tab-pane" id="slideshows">
<div class="row" style="padding-left: 45px;">
<div class="col-md-6 brick">
@@ -347,8 +386,20 @@
<div class="footer-widget">
<h5>Meetups</h5>
<ul class="latest-news">
- <li><a href="http://www.meetup.com/Apache-Storm-Apache-Kafka/">Sunnyvale, CA</a> <span class="small">(10 May 2015)</span></li>
- <li><a href="http://www.meetup.com/Apache-Storm-Kafka-Users/">Seatle, WA</a> <span class="small">(27 Jun 2015)</span></li>
+
+ <li><a href="http://www.meetup.com/Apache-Storm-Apache-Kafka/">Apache Storm & Apache Kafka</a> <span class="small">(Sunnyvale, CA)</span></li>
+
+ <li><a href="http://www.meetup.com/Apache-Storm-Kafka-Users/">Apache Storm & Kafka Users</a> <span class="small">(Seattle, WA)</span></li>
+
+ <li><a href="http://www.meetup.com/New-York-City-Storm-User-Group/">NYC Storm User Group</a> <span class="small">(New York, NY)</span></li>
+
+ <li><a href="http://www.meetup.com/Bay-Area-Stream-Processing">Bay Area Stream Processing</a> <span class="small">(Emeryville, CA)</span></li>
+
+ <li><a href="http://www.meetup.com/Boston-Storm-Users/">Boston Realtime Data</a> <span class="small">(Boston, MA)</span></li>
+
+ <li><a href="http://www.meetup.com/storm-london">London Storm User Group</a> <span class="small">(London, UK)</span></li>
+
+ <!-- <li><a href="http://www.meetup.com/Apache-Storm-Kafka-Users/">Seatle, WA</a> <span class="small">(27 Jun 2015)</span></li> -->
</ul>
</div>
</div>
@@ -384,7 +435,9 @@
<hr/>
<div class="row">
<div class="col-md-12">
- <p align="center">Copyright © 2015 <a href="http://www.apache.org">Apache Software Foundation</a>. All Rights Reserved. Apache Storm, Apache, the Apache feather logo, and the Apache Storm project logos are trademarks of The Apache Software Foundation. All other marks mentioned may be trademarks or registered trademarks of their respective owners.</p>
+ <p align="center">Copyright © 2015 <a href="http://www.apache.org">Apache Software Foundation</a>. All Rights Reserved.
+ <br>Apache Storm, Apache, the Apache feather logo, and the Apache Storm project logos are trademarks of The Apache Software Foundation.
+ <br>All other marks mentioned may be trademarks or registered trademarks of their respective owners.</p>
</div>
</div>
</div>
http://git-wip-us.apache.org/repos/asf/storm/blob/fde3433f/_site/tutorial.html
----------------------------------------------------------------------
diff --git a/_site/tutorial.html b/_site/tutorial.html
index f640f68..695db94 100644
--- a/_site/tutorial.html
+++ b/_site/tutorial.html
@@ -67,9 +67,10 @@
<li><a href="/documentation.html" id="documentation">Documentation</a></li>
<li><a href="/talksAndVideos.html">Talks and Slideshows</a></li>
<li class="dropdown">
- <a href="#" class="dropdown-toggle" data-toggle="dropdown" id="contribute">Contribute <b class="caret"></b></a>
+ <a href="#" class="dropdown-toggle" data-toggle="dropdown" id="contribute">Community <b class="caret"></b></a>
<ul class="dropdown-menu">
- <li><a href="/contribute/Contributing-to-Storm.html">Getting Started</a></li>
+ <li><a href="/contribute/Contributing-to-Storm.html">Contributing</a></li>
+ <li><a href="/contribute/People.html">People</a></li>
<li><a href="/contribute/BYLAWS.html">ByLaws</a></li>
</ul>
</li>
@@ -386,8 +387,20 @@
<div class="footer-widget">
<h5>Meetups</h5>
<ul class="latest-news">
- <li><a href="http://www.meetup.com/Apache-Storm-Apache-Kafka/">Sunnyvale, CA</a> <span class="small">(10 May 2015)</span></li>
- <li><a href="http://www.meetup.com/Apache-Storm-Kafka-Users/">Seatle, WA</a> <span class="small">(27 Jun 2015)</span></li>
+
+ <li><a href="http://www.meetup.com/Apache-Storm-Apache-Kafka/">Apache Storm & Apache Kafka</a> <span class="small">(Sunnyvale, CA)</span></li>
+
+ <li><a href="http://www.meetup.com/Apache-Storm-Kafka-Users/">Apache Storm & Kafka Users</a> <span class="small">(Seattle, WA)</span></li>
+
+ <li><a href="http://www.meetup.com/New-York-City-Storm-User-Group/">NYC Storm User Group</a> <span class="small">(New York, NY)</span></li>
+
+ <li><a href="http://www.meetup.com/Bay-Area-Stream-Processing">Bay Area Stream Processing</a> <span class="small">(Emeryville, CA)</span></li>
+
+ <li><a href="http://www.meetup.com/Boston-Storm-Users/">Boston Realtime Data</a> <span class="small">(Boston, MA)</span></li>
+
+ <li><a href="http://www.meetup.com/storm-london">London Storm User Group</a> <span class="small">(London, UK)</span></li>
+
+ <!-- <li><a href="http://www.meetup.com/Apache-Storm-Kafka-Users/">Seatle, WA</a> <span class="small">(27 Jun 2015)</span></li> -->
</ul>
</div>
</div>
@@ -423,7 +436,9 @@
<hr/>
<div class="row">
<div class="col-md-12">
- <p align="center">Copyright © 2015 <a href="http://www.apache.org">Apache Software Foundation</a>. All Rights Reserved. Apache Storm, Apache, the Apache feather logo, and the Apache Storm project logos are trademarks of The Apache Software Foundation. All other marks mentioned may be trademarks or registered trademarks of their respective owners.</p>
+ <p align="center">Copyright © 2015 <a href="http://www.apache.org">Apache Software Foundation</a>. All Rights Reserved.
+ <br>Apache Storm, Apache, the Apache feather logo, and the Apache Storm project logos are trademarks of The Apache Software Foundation.
+ <br>All other marks mentioned may be trademarks or registered trademarks of their respective owners.</p>
</div>
</div>
</div>
http://git-wip-us.apache.org/repos/asf/storm/blob/fde3433f/contribute/People.md
----------------------------------------------------------------------
diff --git a/contribute/People.md b/contribute/People.md
new file mode 100644
index 0000000..0a7a46c
--- /dev/null
+++ b/contribute/People.md
@@ -0,0 +1,44 @@
+---
+title: People
+layout: documentation
+documentation: true
+---
+
+
+
+## Project Management
+
+
+<table class="table table-striped table-bordered table-responsive">
+ <thead>
+ <th class="">Name</th>
+ <th class="">Role</th>
+ <th class="">Apache ID</th>
+ <th class="">Github</th>
+ </thead>
+{% for committer in site.data.committers %}
+ <tr>
+ <td class="">{{ committer.name}}</td>
+ <td class="">{{committer.roles}}</td>
+ <td class="">{{committer.asfid}}</td>
+ <td class=""><a href="https://github.com/{{committer.github}}">@{{committer.github}}</td>
+ </tr>
+{% endfor %}
+</table>
+
+
+## Contributors
+
+<table class="table table-striped table-bordered table-responsive">
+ <thead>
+ <th class="">Name</th>
+ <th class="">Github</th>
+ </thead>
+{% for contributor in site.data.contributors %}
+ <tr>
+ <td class="">{{ contributor.name}}</td>
+ <td class=""><a href="https://github.com/{{contributor.github}}">@{{contributor.github}}</td>
+ </tr>
+{% endfor %}
+</table>
+
http://git-wip-us.apache.org/repos/asf/storm/blob/fde3433f/downloads.html
----------------------------------------------------------------------
diff --git a/downloads.html b/downloads.html
index fd10e39..9eaf444 100644
--- a/downloads.html
+++ b/downloads.html
@@ -32,28 +32,28 @@ older:
<h3>Current Beta Release</h3>
The current beta release is 0.10.0-beta1. Source and binary distributions can be found below.
- The list of changes for this release can be found <a href="https://github.com/apache/storm/blob/v0.10.0-beta/CHANGELOG.md">here.</a>
+ The list of changes for this release can be found <a href="https://github.com/apache/storm/blob/v0.10.0-beta1/CHANGELOG.md">here.</a>
<ul>
- <li><a href="http://www.apache.org/dyn/closer.cgi/storm/apache-storm-0.10.0-beta/apache-storm-0.10.0-beta.tar.gz">apache-storm-0.10.0-beta.tar.gz</a>
- [<a href="http://www.us.apache.org/dist/storm/apache-storm-0.10.0-beta/apache-storm-0.10.0-beta.tar.gz.asc">PGP</a>]
- [<a href="http://www.us.apache.org/dist/storm/apache-storm-0.10.0-beta/apache-storm-0.10.0-beta.tar.gz.sha">SHA512</a>]
- [<a href="http://www.us.apache.org/dist/storm/apache-storm-0.10.0-beta/apache-storm-0.10.0-beta.tar.gz.md5">MD5</a>]
- </li>
- <li><a href="http://www.apache.org/dyn/closer.cgi/storm/apache-storm-0.10.0-beta/apache-storm-0.10.0-beta.zip">apache-storm-0.10.0-beta.zip</a>
- [<a href="http://www.us.apache.org/dist/storm/apache-storm-0.10.0-beta/apache-storm-0.10.0-beta.zip.asc">PGP</a>]
- [<a href="http://www.us.apache.org/dist/storm/apache-storm-0.10.0-beta/apache-storm-0.10.0-beta.zip.sha">SHA512</a>]
- [<a href="http://www.us.apache.org/dist/storm/apache-storm-0.10.0-beta/apache-storm-0.10.0-beta.zip.md5">MD5</a>]
- </li>
- <li><a href="http://www.apache.org/dyn/closer.cgi/storm/apache-storm-0.10.0-beta/apache-storm-0.10.0-beta-src.tar.gz">apache-storm-0.10.0-beta-src.tar.gz</a>
- [<a href="http://www.us.apache.org/dist/storm/apache-storm-0.10.0-beta/apache-storm-0.10.0-beta-src.tar.gz.asc">PGP</a>]
- [<a href="http://www.us.apache.org/dist/storm/apache-storm-0.10.0-beta/apache-storm-0.10.0-beta-src.tar.gz.sha">SHA512</a>]
- [<a href="http://www.us.apache.org/dist/storm/apache-storm-0.10.0-beta/apache-storm-0.10.0-beta-src.tar.gz.md5">MD5</a>]
- </li>
- <li><a href="http://www.apache.org/dyn/closer.cgi/storm/apache-storm-0.10.0-beta/apache-storm-0.10.0-beta-src.zip">apache-storm-0.10.0-beta-src.zip</a>
- [<a href="http://www.us.apache.org/dist/storm/apache-storm-0.10.0-beta/apache-storm-0.10.0-beta-src.zip.asc">PGP</a>]
- [<a href="http://www.us.apache.org/dist/storm/apache-storm-0.10.0-beta/apache-storm-0.10.0-beta-src.zip.sha">SHA512</a>]
- [<a href="http://www.us.apache.org/dist/storm/apache-storm-0.10.0-beta/apache-storm-0.10.0-beta-src.zip.md5">MD5</a>]
+ <li><a href="http://www.apache.org/dyn/closer.cgi/storm/apache-storm-0.10.0-beta1/apache-storm-0.10.0-beta1.tar.gz">apache-storm-0.10.0-beta1.tar.gz</a>
+ [<a href="http://www.us.apache.org/dist/storm/apache-storm-0.10.0-beta1/apache-storm-0.10.0-beta1.tar.gz.asc">PGP</a>]
+ [<a href="http://www.us.apache.org/dist/storm/apache-storm-0.10.0-beta1/apache-storm-0.10.0-beta1.tar.gz.sha">SHA512</a>]
+ [<a href="http://www.us.apache.org/dist/storm/apache-storm-0.10.0-beta1/apache-storm-0.10.0-beta1.tar.gz.md5">MD5</a>]
+ </li>
+ <li><a href="http://www.apache.org/dyn/closer.cgi/storm/apache-storm-0.10.0-beta1/apache-storm-0.10.0-beta1.zip">apache-storm-0.10.0-beta1.zip</a>
+ [<a href="http://www.us.apache.org/dist/storm/apache-storm-0.10.0-beta1/apache-storm-0.10.0-beta1.zip.asc">PGP</a>]
+ [<a href="http://www.us.apache.org/dist/storm/apache-storm-0.10.0-beta1/apache-storm-0.10.0-beta1.zip.sha">SHA512</a>]
+ [<a href="http://www.us.apache.org/dist/storm/apache-storm-0.10.0-beta1/apache-storm-0.10.0-beta1.zip.md5">MD5</a>]
+ </li>
+ <li><a href="http://www.apache.org/dyn/closer.cgi/storm/apache-storm-0.10.0-beta1/apache-storm-0.10.0-beta1-src.tar.gz">apache-storm-0.10.0-beta1-src.tar.gz</a>
+ [<a href="http://www.us.apache.org/dist/storm/apache-storm-0.10.0-beta1/apache-storm-0.10.0-beta1-src.tar.gz.asc">PGP</a>]
+ [<a href="http://www.us.apache.org/dist/storm/apache-storm-0.10.0-beta1/apache-storm-0.10.0-beta1-src.tar.gz.sha">SHA512</a>]
+ [<a href="http://www.us.apache.org/dist/storm/apache-storm-0.10.0-beta1/apache-storm-0.10.0-beta1-src.tar.gz.md5">MD5</a>]
+ </li>
+ <li><a href="http://www.apache.org/dyn/closer.cgi/storm/apache-storm-0.10.0-beta1/apache-storm-0.10.0-beta1-src.zip">apache-storm-0.10.0-beta1-src.zip</a>
+ [<a href="http://www.us.apache.org/dist/storm/apache-storm-0.10.0-beta1/apache-storm-0.10.0-beta1-src.zip.asc">PGP</a>]
+ [<a href="http://www.us.apache.org/dist/storm/apache-storm-0.10.0-beta1/apache-storm-0.10.0-beta1-src.zip.sha">SHA512</a>]
+ [<a href="http://www.us.apache.org/dist/storm/apache-storm-0.10.0-beta1/apache-storm-0.10.0-beta1-src.zip.md5">MD5</a>]
</li>
</ul>
http://git-wip-us.apache.org/repos/asf/storm/blob/fde3433f/index.html
----------------------------------------------------------------------
diff --git a/index.html b/index.html
index 7398b84..4acccc5 100644
--- a/index.html
+++ b/index.html
@@ -1,6 +1,6 @@
---
layout: default
-title: Storm Home
+title: Apache Storm
---
<div class="content">
@@ -23,16 +23,9 @@ title: Storm Home
<h4>Latest News</h4>
<ul class="latest-news">
<ul class="latest-news">
- {% for post in site.posts limit:7 %}
- <li><a href="{{ post.url }}">{{ post.title }}</a> <span class="small">({{ post.date | date_to_string }}) </span></li>
- {% endfor %}
- <!-- <li><a href="/2015/06/15/storm0100-beta-released.html">Storm 0.10.0-beta Released</a> <span class="small">(15 Jun 2015)</span></li>
- <li><a href="/2015/06/04/storm095-released.html">Storm 0.9.5 released</a> <span class="small">(04 Jun 2015)</span></li>
- <li><a href="/2015/03/25/storm094-released.html">Storm 0.9.4 released</a> <span class="small">(25 Mar 2015)</span></li>
- <li><a href="/2014/11/25/storm093-released.html">Storm 0.9.3 released</a> <span class="small">(25 Nov 2014)</span></li>
- <li><a href="/2014/10/20/storm093-release-candidate.html">Storm 0.9.3 release candidate 1 available</a> <span class="small">(20 Oct 2014)</span></li>
- <li><a href="/2014/06/25/storm092-released.html">Storm 0.9.2 released</a> <span class="small">(25 Jun 2014)</span></li>
- <li><a href="/2014/06/17/contest-results.html">Storm Logo Contest Results</a> <span class="small">(17 Jun 2014)</span></li> -->
+ {% for post in site.posts limit:7 %}
+ <li><a href="{{ post.url }}">{{ post.title }}</a> <span class="small">({{ post.date | date_to_string }}) </span></li>
+ {% endfor %}
</ul>
<p align="right"><a href="{{ site.posts[0].url }}" class="btn-std">More News</a></p>
</div>
http://git-wip-us.apache.org/repos/asf/storm/blob/fde3433f/talksAndVideos.md
----------------------------------------------------------------------
diff --git a/talksAndVideos.md b/talksAndVideos.md
index 7577173..7b1c727 100644
--- a/talksAndVideos.md
+++ b/talksAndVideos.md
@@ -14,43 +14,18 @@ documentation: true
<div class="tab-content">
<div role="tabpanel" class="tab-pane active" id="talks">
+
+
+
+
+<!-- ################### -->
<div class="brickSS">
<div class="row">
<div class="col-md-6">
- <iframe width="560" height="315" src="https://www.youtube.com/embed/LpNbjXFPyZ0" frameborder="0" allowfullscreen></iframe>
- </div>
- <div class="col-md-6">
- <h3>Storm: distributed and fault-tolerant realtime computation from
- <span>
- nathanmarz
- </span>
- </h3>
+ <h3>Andrew Montalenti - streamparse: real-time streams with Python and Apache Storm - PyCon 2015</h3>
<div>
- <p>Published on Nov 29, 2013
- This is a technical architect's case study of how Loggly has employed the latest social-media-scale technologies as the backbone ingestion processing for our multi-tenant, geo-distributed, and real-time log management system. This presentation describes design details of how we built a second-generation system fully leveraging AWS services including Amazon Route 53 DNS with heartbeat and latency-based routing, multi-region VPCs, Elastic Load Balancing, Amazon Relational Database Service, and a number of pro-active and re-active approaches to scaling computational and indexing capacity.
- The talk includes lessons learned in our first generation release, validated by thousands of customers; speed bumps and the mistakes we made along the way; various data models and architectures previously considered; and success at scale: speeds, feeds, and an unmeltable log processing engine.</p>
- </div>
- </div>
- </div>
- </div>
-
- <div class="brickSS">
- <div class="row">
- <div class="col-md-6">
- <h3>Apache Storm for Real-Time Processing in Hadoop
- <span>
- Hortonworks
- </span>
- </h3>
- <div>
- <p>Published on Jan 29, 2014
- In this video, we cover:
-
- -- 5 key benefits of Apache Storm
- -- The reasons for adding Apache Storm to Hortonworks Data Platform
- -- How Apache Hadoop YARN opened the door for integration of Storm into Hadoop
- -- Two general use case patterns with Storm and specific uses in transportation and advertising
- -- How Ambari will provide a single view and common operational platform for enterprises to distribute cluster resources across different workloads</p>
+ <p>Published on Apr 12, 2015</p><p>
+ Real-time streams are everywhere, but does Python have a good way of processing them? Until recently, there were no good options. A new open source project, streamparse, makes working with real-time data streams easy for Pythonistas. If you have ever wondered how to process 10,000 data tuples per second with Python -- while maintaining high availability and low latency -- this talk is for you.</p>
</div>
</div>
<div class="col-md-6">
@@ -59,6 +34,8 @@ documentation: true
</div>
</div>
+<!-- ################### -->
+
<div class="brickSS">
<div class="row">
<div class="col-md-6">
@@ -66,12 +43,9 @@ documentation: true
</div>
<div class="col-md-6">
<h3>Real-Time Big Data Analytics with Storm
- <span>
- Aerospike
- </span>
</h3>
<div>
- <p>Published on Oct 12, 2013
+ <p>Published on Oct 12, 2013</p><p>
This talk provides an overview of the open source Storm system for processing Big Data in realtime. The talk starts with an overview of the technology, including key components: Nimbus, Zookeeper, Topology, Tuple, Trident. The presentation then dives into the complex Big Data architecture in which Storm can be integrated. The result is a compelling stack of technologies including integrated Hadoop clusters, MPP, and NoSQL databases.
The presentation then reviews real world use cases for realtime Big Data analytics. Social updates, in particular real-time news feeds on sites like Twitter and Facebook, benefit from Storm's capacity to process benefits from distributed logic of streaming. Another case study is financial compliance monitoring, where Storm plays a primary role in reducing the market data to a useable subset in realtime. In a final use case, Storm is crucial to collect rich operational intelligence, because it builds multidimensional stats and executes distributed queries.</p>
@@ -80,58 +54,30 @@ documentation: true
</div>
</div>
- <div class="brickSS">
- <div class="row">
- <div class="col-md-6">
- <h3>ETE 2012 - Nathan Marz on Storm
- <span>
- ChariotSolutions
- </span>
- </h3>
- <div>
- <p>Published on May 15, 2012
- "Storm makes it easy to write and scale complex realtime computations on a cluster of computers, doing for realtime processing what Hadoop did for batch processing. Storm guarantees that every message will be processed. And it's fast -- you can process millions of messages per second with a small cluster. Best of all, you can write Storm topologies using any programming language. Storm was open-sourced by Twitter in September of 2011 and has since been adopted by numerous companies around the world.
- Storm provides a small set of simple, easy to understand primitives. These primitives can be used to solve a stunning number of realtime computation problems, from stream processing to continuous computation to distributed RPC. In this talk you'll learn:
-
- - The concepts of Storm: streams, spouts, bolts, and topologies
- - Developing and testing topologies using Storm's local mode
- - Deploying topologies on Storm clusters
- - How Storm achieves fault-tolerance and guarantees data processing
- - Computing intense functions on the fly in parallel using Distributed RPC
- - Making realtime computations idempotent using transactional topologies
- - Examples of production usage of Storm</p>
- </div>
- </div>
- <div class="col-md-6">
- <iframe width="420" height="315" src="https://www.youtube.com/embed/bdps8tE0gYo" frameborder="0" allowfullscreen></iframe>
- </div>
- </div>
- </div>
+<!-- ################### -->
+
<div class="brickSS">
<div class="row">
<div class="col-md-6">
<iframe width="560" height="315" src="https://www.youtube.com/embed/od8U-XijzlQ" frameborder="0" allowfullscreen></iframe>
</div>
<div class="col-md-6">
- <h3>Andrew Montalenti & Keith Bourgoin - Real-time streams and logs with Storm and Kafka
- <span>
- PyData
- </span>
+ <h3>Andrew Montalenti & Keith Bourgoin - Real-time streams and logs with Storm and Kafka - PyData SV 2014
</h3>
<div>
- <p>Published on Jun 12, 2014
- PyData SV 2014
- Some of the biggest issues at the center of analyzing large amounts of data are query flexibility, latency, and fault tolerance. Modern technologies that build upon the success of "big data" platforms, such as Apache Hadoop, have made it possible to spread the load of data analysis to commodity machines, but these analyses can still take hours to run and do not respond well to rapidly-changing data sets.
+ <p>Published on Jun 12, 2014</p><p>
+
+ Some of the biggest issues at the center of analyzing large amounts of data are query flexibility, latency, and fault tolerance. Modern technologies that build upon the success of "big data" platforms, such as Apache Hadoop, have made it possible to spread the load of data analysis to commodity machines, but these analyses can still take hours to run and do not respond well to rapidly-changing data sets.</p>
- A new generation of data processing platforms -- which we call "stream architectures" -- have converted data sources into streams of data that can be processed and analyzed in real-time. This has led to the development of various distributed real-time computation frameworks (e.g. Apache Storm) and multi-consumer data integration technologies (e.g. Apache Kafka). Together, they offer a way to do predictable computation on real-time data streams.
+ <p>A new generation of data processing platforms -- which we call "stream architectures" -- have converted data sources into streams of data that can be processed and analyzed in real-time. This has led to the development of various distributed real-time computation frameworks (e.g. Apache Storm) and multi-consumer data integration technologies (e.g. Apache Kafka). Together, they offer a way to do predictable computation on real-time data streams.</p>
- In this talk, we will give an overview of these technologies and how they fit into the Python ecosystem. This will include a discussion of current open source interoperability options with Python, and how to combine real-time computation with batch logic written for Hadoop. We will also discuss Kafka and Storm's alternatives, current industry usage, and some real-world examples of how these technologies are being used in production by Parse.ly today.</p>
+ <p>In this talk, we will give an overview of these technologies and how they fit into the Python ecosystem. This will include a discussion of current open source interoperability options with Python, and how to combine real-time computation with batch logic written for Hadoop. We will also discuss Kafka and Storm's alternatives, current industry usage, and some real-world examples of how these technologies are being used in production by Parse.ly today.</p>
</div>
</div>
</div>
</div>
-
+<!-- ################### -->
<div class="brickSS">
<div class="row">
<div class="col-md-6">
@@ -141,12 +87,12 @@ documentation: true
</span>
</h3>
<div>
- <p>Published on Sep 18, 2014
- Bobby Evans and Tom Graves, the engineering leads for Spark and Storm development at Yahoo will talk about how these technologies are used on Yahoo's grids and reasons why to use one or the other.
+ <p>Published on Sep 18, 2014</p><p>
+ Bobby Evans and Tom Graves, the engineering leads for Spark and Storm development at Yahoo will talk about how these technologies are used on Yahoo's grids and reasons why to use one or the other.</p>
- Bobby Evans is the low latency data processing architect at Yahoo. He is a PMC member on many Apache projects including Storm, Hadoop, Spark, and Tez. His team is responsible for delivering Storm as a service to all of Yahoo and maintaining Spark on Yarn for Yahoo (Although Tom really does most of that work).
+ <p>Bobby Evans is the low latency data processing architect at Yahoo. He is a PMC member on many Apache projects including Storm, Hadoop, Spark, and Tez. His team is responsible for delivering Storm as a service to all of Yahoo and maintaining Spark on Yarn for Yahoo (Although Tom really does most of that work).</p>
- Tom Graves a Senior Software Engineer on the Platform team at Yahoo. He is an Apache PMC member on Hadoop, Spark, and Tez. His team is responsible for delivering and maintaining Spark on Yarn for Yahoo.</p>
+ <p>Tom Graves a Senior Software Engineer on the Platform team at Yahoo. He is an Apache PMC member on Hadoop, Spark, and Tez. His team is responsible for delivering and maintaining Spark on Yarn for Yahoo.</p>
</div>
</div>
<div class="col-md-6">
@@ -155,7 +101,7 @@ documentation: true
</div>
</div>
-
+<!-- ################### -->
<div class="brickSS">
<div class="row">
<div class="col-md-6">
@@ -168,24 +114,116 @@ documentation: true
</span>
</h3>
<div>
- <p>Published on Apr 3, 2014
- This talk was presented at the New York City Storm User Group hosted by Spotify on March 25, 2014. More info here: http://www.hakkalabs.co/articles/stor...
+ <p>Published on Apr 3, 2014</p><p>
+ This talk was presented at the New York City Storm User Group hosted by Spotify on March 25, 2014.</p><p>
This is the first time that a Spotify engineer has spoken publicly about their deployment and use cases for Storm! In this talk, Software Engineer Neville Li describes:
+ <ul><li>
+ Real-time features developed using Storm and Kafka including recommendations, social features, data visualization and ad targeting</li>
- Real-time features developed using Storm and Kafka including recommendations, social features, data visualization and ad targeting
+ <li>Architecture</li>
- Architecture
+ <li>Production integration</li>
- Production integration
+ <li>Best practices for deployment</li></ul></p>
+ <p>Spotify is an exciting case study - users create 600 Gigabyte of data per day and 150 Gigabyte of data per day via different services. Every day 4 Terabyte of data is generated in Hadoop, a 700-node cluster running over 2.000 jobs per day. They currently have 28 Petabytes of storage, spread out over 4 data centres across the world.</p>
+ </div>
+ </div>
+ </div>
+ </div>
+
+<!-- ################### -->
+<!-- ################### -->
+ <div class="brickSS">
+ <div class="row">
+ <div class="col-md-6">
+ <h3>Nathan Bijnens: A Real-Time Architecture Using Hadoop & Storm</h3>
+ <div>
+ <p>Published on Dec 19, 2013</p>
+ <p>With the proliferation of data sources and growing user bases, the amount of data generated requires new ways for storage and processing. Hadoop opened new possibilities, yet it falls short of instant delivery. Adding stream processing using Nathan Marz's Storm, can overcome this delay and bridge the gap to real-time aggregation and reporting. On the Batch layer all master data is kept and is immutable. Once the base data is stored a recurring process will index the data. This process reads all master data, parses it and will create new views out of it. The new views will replace all previously created views. In the Speed layer data is stored not yet absorbed in the Batch layer. Hours of data instead of years of data. Once the data is indexed in the Batch layer the data can discarded in the Speed layer. The Query Service merges the data from the Speed and Batch layers. This talk focuses on the Lambda architecture, which combines multiple technologi
es to be able to process vast amounts of data, while still being able to react timely and report near real-time statistics. Filmed at JAX London 2013.</p>
+ </div>
+ </div>
+ <div class="col-md-6">
+ <iframe width="560" height="315" src="https://www.youtube.com/embed/CrABmVi12_A" frameborder="0" allowfullscreen></iframe>
+ </div>
+
+ </div>
+ </div>
+<!-- ################### -->
- Best practices for deployment</p>
+ <div class="brickSS">
+ <div class="row">
+ <div class="col-md-6">
+ <h3>Infrastructure at Scale: Apache Kafka, Twitter Storm & Elastic Search</h3>
+ <div>
+ <p>Published on Nov 29, 2013</p><p>
+ This is a technical architect's case study of how Loggly has employed the latest social-media-scale technologies as the backbone ingestion processing for our multi-tenant, geo-distributed, and real-time log management system. This presentation describes design details of how we built a second-generation system fully leveraging AWS services including Amazon Route 53 DNS with heartbeat and latency-based routing, multi-region VPCs, Elastic Load Balancing, Amazon Relational Database Service, and a number of pro-active and re-active approaches to scaling computational and indexing capacity.</p>
+ <p>
+ The talk includes lessons learned in our first generation release, validated by thousands of customers; speed bumps and the mistakes we made along the way; various data models and architectures previously considered; and success at scale: speeds, feeds, and an unmeltable log processing engine.</p>
</div>
</div>
+ <div class="col-md-6">
+ <iframe width="560" height="315" src="https://www.youtube.com/embed/LpNbjXFPyZ0" frameborder="0" allowfullscreen></iframe>
+ </div>
+
</div>
</div>
+<!-- ################### -->
+<!-- ################### -->
+
+ <div class="brickSS">
+ <div class="row">
+ <div class="col-md-6">
+ <iframe width="560" height="315" src="https://www.youtube.com/embed/hVO5nbxnBkU" frameborder="0" allowfullscreen></iframe>
+ </div>
+ <div class="col-md-6">
+ <h3>Real-Time Big Data Analytics with Storm
+ </h3>
+ <div>
+ <p>Published on Oct 12, 2013</p><p>
+ This talk provides an overview of the open source Storm system for processing Big Data in realtime. The talk starts with an overview of the technology, including key components: Nimbus, Zookeeper, Topology, Tuple, Trident. The presentation then dives into the complex Big Data architecture in which Storm can be integrated. The result is a compelling stack of technologies including integrated Hadoop clusters, MPP, and NoSQL databases.</p>
+ <p>
+ The presentation then reviews real world use cases for realtime Big Data analytics. Social updates, in particular real-time news feeds on sites like Twitter and Facebook, benefit from Storm's capacity to process benefits from distributed logic of streaming. Another case study is financial compliance monitoring, where Storm plays a primary role in reducing the market data to a useable subset in realtime. In a final use case, Storm is crucial to collect rich operational intelligence, because it builds multidimensional stats and executes distributed queries.</p>
+ </div>
+ </div>
+ </div>
+ </div>
+
+<!-- ################### -->
+
+
+<!-- ################### -->
+ <div class="brickSS">
+ <div class="row">
+ <div class="col-md-6">
+ <h3>ETE 2012: Nathan Marz on Storm
+ </h3>
+ <div>
+ <p>Published on May 15, 2012</p><p>
+
+ Storm makes it easy to write and scale complex realtime computations on a cluster of computers, doing for realtime processing what Hadoop did for batch processing. Storm guarantees that every message will be processed. And it's fast -- you can process millions of messages per second with a small cluster. Best of all, you can write Storm topologies using any programming language. Storm was open-sourced by Twitter in September of 2011 and has since been adopted by numerous companies around the world.</p><p>
+ Storm provides a small set of simple, easy to understand primitives. These primitives can be used to solve a stunning number of realtime computation problems, from stream processing to continuous computation to distributed RPC. In this talk you'll learn:
+ <ul>
+ <li>The concepts of Storm: streams, spouts, bolts, and topologies</li>
+ <li>Developing and testing topologies using Storm's local mode</li>
+ <li>Deploying topologies on Storm clusters</li>
+ <li>How Storm achieves fault-tolerance and guarantees data processing</li>
+ <li>Computing intense functions on the fly in parallel using Distributed RPC</li>
+ <li>Making realtime computations idempotent using transactional topologies</li>
+ <li>Examples of production usage of Storm</li></ul></p>
+ </div>
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