際際滷shows by User: RyanBosshart / http://www.slideshare.net/images/logo.gif 際際滷shows by User: RyanBosshart / Fri, 27 May 2016 19:55:12 GMT 際際滷Share feed for 際際滷shows by User: RyanBosshart Kudu - Fast Analytics on Fast Data /slideshow/kudu-fast-analytics-on-fast-data-62478776/62478776 kuduformeetup-160527195512
If you're building relational, time-series, IOT, or real-time architectures using Hadoop, you will find Apache Kudu an attractive choice. With Kudu, you'll be able to build your applications more simply and with fewer moving parts. Hadoop has become faster and more capable, and has continued to narrow the gap compared to traditional database technologies. However, for developers looking for up-to-the-second analytics on fast-moving data, some important gaps remain that prevent many applications from transitioning to Hadoop-based architectures. Users are often caught between a rock and a hard place: columnar formats such as Apache Parquet offer extremely fast scan rates for analytics, but little to no ability for real-time modification or row-by-row indexed access. Online systems such as HBase offer very fast random access, but scan rates that are too slow for large scale data warehousing and analytical workloads. This talk will describe Kudu, the new addition to the open source Hadoop ecosystem with out-of-the-box integration with Apache Spark and Apache Impala. Kudu fills the gap described above to provide a new option to achieve fast scans and fast random access from a single API.]]>

If you're building relational, time-series, IOT, or real-time architectures using Hadoop, you will find Apache Kudu an attractive choice. With Kudu, you'll be able to build your applications more simply and with fewer moving parts. Hadoop has become faster and more capable, and has continued to narrow the gap compared to traditional database technologies. However, for developers looking for up-to-the-second analytics on fast-moving data, some important gaps remain that prevent many applications from transitioning to Hadoop-based architectures. Users are often caught between a rock and a hard place: columnar formats such as Apache Parquet offer extremely fast scan rates for analytics, but little to no ability for real-time modification or row-by-row indexed access. Online systems such as HBase offer very fast random access, but scan rates that are too slow for large scale data warehousing and analytical workloads. This talk will describe Kudu, the new addition to the open source Hadoop ecosystem with out-of-the-box integration with Apache Spark and Apache Impala. Kudu fills the gap described above to provide a new option to achieve fast scans and fast random access from a single API.]]>
Fri, 27 May 2016 19:55:12 GMT /slideshow/kudu-fast-analytics-on-fast-data-62478776/62478776 RyanBosshart@slideshare.net(RyanBosshart) Kudu - Fast Analytics on Fast Data RyanBosshart If you're building relational, time-series, IOT, or real-time architectures using Hadoop, you will find Apache Kudu an attractive choice. With Kudu, you'll be able to build your applications more simply and with fewer moving parts. Hadoop has become faster and more capable, and has continued to narrow the gap compared to traditional database technologies. However, for developers looking for up-to-the-second analytics on fast-moving data, some important gaps remain that prevent many applications from transitioning to Hadoop-based architectures. Users are often caught between a rock and a hard place: columnar formats such as Apache Parquet offer extremely fast scan rates for analytics, but little to no ability for real-time modification or row-by-row indexed access. Online systems such as HBase offer very fast random access, but scan rates that are too slow for large scale data warehousing and analytical workloads. This talk will describe Kudu, the new addition to the open source Hadoop ecosystem with out-of-the-box integration with Apache Spark and Apache Impala. Kudu fills the gap described above to provide a new option to achieve fast scans and fast random access from a single API. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/kuduformeetup-160527195512-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> If you&#39;re building relational, time-series, IOT, or real-time architectures using Hadoop, you will find Apache Kudu an attractive choice. With Kudu, you&#39;ll be able to build your applications more simply and with fewer moving parts. Hadoop has become faster and more capable, and has continued to narrow the gap compared to traditional database technologies. However, for developers looking for up-to-the-second analytics on fast-moving data, some important gaps remain that prevent many applications from transitioning to Hadoop-based architectures. Users are often caught between a rock and a hard place: columnar formats such as Apache Parquet offer extremely fast scan rates for analytics, but little to no ability for real-time modification or row-by-row indexed access. Online systems such as HBase offer very fast random access, but scan rates that are too slow for large scale data warehousing and analytical workloads. This talk will describe Kudu, the new addition to the open source Hadoop ecosystem with out-of-the-box integration with Apache Spark and Apache Impala. Kudu fills the gap described above to provide a new option to achieve fast scans and fast random access from a single API.
Kudu - Fast Analytics on Fast Data from Ryan Bosshart
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Realtime Detection of DDOS attacks using Apache Spark and MLLib /slideshow/realtime-detection-of-ddos-attacks-using-apache-spark-and-mllib/55398026 realtimearchitecturestalk-nov2015copy-151123012321-lva1-app6891
In this talk we will show how Hadoop Ecosystem tools like Apache Kafka, Spark, and MLLib can be used in various real-time architectures and how they can be used to perform real-time detection of a DDOS attack. We will explain some of the challenges in building real-time architectures, followed by walking through the DDOS detection example and a live demo. This talk is appropriate for anyone interested in Security, IoT, Apache Kafka, Spark, or Hadoop. Presenter Ryan Bosshart is a Systems Engineer at Cloudera and is the first 3 time presenter at BigDataMadison!]]>

In this talk we will show how Hadoop Ecosystem tools like Apache Kafka, Spark, and MLLib can be used in various real-time architectures and how they can be used to perform real-time detection of a DDOS attack. We will explain some of the challenges in building real-time architectures, followed by walking through the DDOS detection example and a live demo. This talk is appropriate for anyone interested in Security, IoT, Apache Kafka, Spark, or Hadoop. Presenter Ryan Bosshart is a Systems Engineer at Cloudera and is the first 3 time presenter at BigDataMadison!]]>
Mon, 23 Nov 2015 01:23:21 GMT /slideshow/realtime-detection-of-ddos-attacks-using-apache-spark-and-mllib/55398026 RyanBosshart@slideshare.net(RyanBosshart) Realtime Detection of DDOS attacks using Apache Spark and MLLib RyanBosshart In this talk we will show how Hadoop Ecosystem tools like Apache Kafka, Spark, and MLLib can be used in various real-time architectures and how they can be used to perform real-time detection of a DDOS attack. We will explain some of the challenges in building real-time architectures, followed by walking through the DDOS detection example and a live demo. This talk is appropriate for anyone interested in Security, IoT, Apache Kafka, Spark, or Hadoop. Presenter Ryan Bosshart is a Systems Engineer at Cloudera and is the first 3 time presenter at BigDataMadison! <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/realtimearchitecturestalk-nov2015copy-151123012321-lva1-app6891-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> In this talk we will show how Hadoop Ecosystem tools like Apache Kafka, Spark, and MLLib can be used in various real-time architectures and how they can be used to perform real-time detection of a DDOS attack. We will explain some of the challenges in building real-time architectures, followed by walking through the DDOS detection example and a live demo. This talk is appropriate for anyone interested in Security, IoT, Apache Kafka, Spark, or Hadoop. Presenter Ryan Bosshart is a Systems Engineer at Cloudera and is the first 3 time presenter at BigDataMadison!
Realtime Detection of DDOS attacks using Apache Spark and MLLib from Ryan Bosshart
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Spark meetup TCHUG /slideshow/spark-meetup-tchug/39445733 sparkmeetup-140923162633-phpapp02
http://bit.ly/RCtaTI]]>

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Tue, 23 Sep 2014 16:26:33 GMT /slideshow/spark-meetup-tchug/39445733 RyanBosshart@slideshare.net(RyanBosshart) Spark meetup TCHUG RyanBosshart http://bit.ly/RCtaTI <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/sparkmeetup-140923162633-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> http://bit.ly/RCtaTI
Spark meetup TCHUG from Ryan Bosshart
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April Big Data Milwaukee - Hands On Session /slideshow/april-big-data-milwaukee-hands-on-session/33303238 bigdatamkeapril-140408224200-phpapp02
Hands on session for HDFS, MapReduce, Hive, and Impala.]]>

Hands on session for HDFS, MapReduce, Hive, and Impala.]]>
Tue, 08 Apr 2014 22:42:00 GMT /slideshow/april-big-data-milwaukee-hands-on-session/33303238 RyanBosshart@slideshare.net(RyanBosshart) April Big Data Milwaukee - Hands On Session RyanBosshart Hands on session for HDFS, MapReduce, Hive, and Impala. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/bigdatamkeapril-140408224200-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Hands on session for HDFS, MapReduce, Hive, and Impala.
April Big Data Milwaukee - Hands On Session from Ryan Bosshart
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Pig Hands On November /RyanBosshart/pig-hands-on-november bigdatamadisonnovember-131121185327-phpapp01
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Thu, 21 Nov 2013 18:53:27 GMT /RyanBosshart/pig-hands-on-november RyanBosshart@slideshare.net(RyanBosshart) Pig Hands On November RyanBosshart <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/bigdatamadisonnovember-131121185327-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br>
Pig Hands On November from Ryan Bosshart
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Hadoop hands on madison /slideshow/hadoop-hands-on-madison-27514361/27514361 hadoophands-onmadison-131023220815-phpapp02
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Wed, 23 Oct 2013 22:08:15 GMT /slideshow/hadoop-hands-on-madison-27514361/27514361 RyanBosshart@slideshare.net(RyanBosshart) Hadoop hands on madison RyanBosshart <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/hadoophands-onmadison-131023220815-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br>
Hadoop hands on madison from Ryan Bosshart
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https://cdn.slidesharecdn.com/profile-photo-RyanBosshart-48x48.jpg?cb=1589846700 https://cdn.slidesharecdn.com/ss_thumbnails/kuduformeetup-160527195512-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/kudu-fast-analytics-on-fast-data-62478776/62478776 Kudu - Fast Analytics ... https://cdn.slidesharecdn.com/ss_thumbnails/realtimearchitecturestalk-nov2015copy-151123012321-lva1-app6891-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/realtime-detection-of-ddos-attacks-using-apache-spark-and-mllib/55398026 Realtime Detection of ... https://cdn.slidesharecdn.com/ss_thumbnails/sparkmeetup-140923162633-phpapp02-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/spark-meetup-tchug/39445733 Spark meetup TCHUG