�ݺ�ߣshows by User: harisr1234

�ݺ�ߣshows by User: harisr1234 / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: harisr1234 / Thu, 08 Jun 2017 00:10:15 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: harisr1234 Streamsets and spark in Retail /slideshow/streamsets-and-spark-in-retail/76747581 streamsetsandspark-retail-170608001015
Figuring out analytics in Retail Centers using Streamsets and Spark.]]>
Figuring out analytics in Retail Centers using Streamsets and Spark.]]> Thu, 08 Jun 2017 00:10:15 GMT /slideshow/streamsets-and-spark-in-retail/76747581 harisr1234@slideshare.net(harisr1234) Streamsets and spark in Retail harisr1234 Figuring out analytics in Retail Centers using Streamsets and Spark. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/streamsetsandspark-retail-170608001015-thumbnail.jpg?width=120&height=120&fit=bounds" /> Figuring out analytics in Retail Centers using Streamsets and Spark.

Streamsets and spark in Retail from Hari Shreedharan

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0 Streamsets and spark at SF Hadoop User Group /slideshow/streamsets-and-spark-at-sf-hadoop-user-group/76747542 streamsetsandsparksfhug-170608000826
A discussion of the Spark integration in Streamsets Data Collector, and how scalability of clusters can be accomplished using SDC and Spark.]]>
A discussion of the Spark integration in Streamsets Data Collector, and how scalability of clusters can be accomplished using SDC and Spark.]]> Thu, 08 Jun 2017 00:08:26 GMT /slideshow/streamsets-and-spark-at-sf-hadoop-user-group/76747542 harisr1234@slideshare.net(harisr1234) Streamsets and spark at SF Hadoop User Group harisr1234 A discussion of the Spark integration in Streamsets Data Collector, and how scalability of clusters can be accomplished using SDC and Spark. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/streamsetsandsparksfhug-170608000826-thumbnail.jpg?width=120&height=120&fit=bounds" /> A discussion of the Spark integration in Streamsets Data Collector, and how scalability of clusters can be accomplished using SDC and Spark.

Streamsets and spark at SF Hadoop User Group from Hari Shreedharan

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0 Streamsets and spark /slideshow/streamsets-and-spark-72763672/72763672 streamsetsandspark-170303063730
Talk at Silicon Valley Ingest Meetup, March 2, 2017]]>
Talk at Silicon Valley Ingest Meetup, March 2, 2017]]> Fri, 03 Mar 2017 06:37:30 GMT /slideshow/streamsets-and-spark-72763672/72763672 harisr1234@slideshare.net(harisr1234) Streamsets and spark harisr1234 Talk at Silicon Valley Ingest Meetup, March 2, 2017 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/streamsetsandspark-170303063730-thumbnail.jpg?width=120&height=120&fit=bounds" /> Talk at Silicon Valley Ingest Meetup, March 2, 2017

Streamsets and spark from Hari Shreedharan

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0 Spark+flume seattle /slideshow/sparkflume-seattle/45780021 sparkflumeseattle-150312205641-conversion-gate01
This is the talk I gave at the Big Data Meetup in Seattle in March. In this talk, I discuss the fundamentals of Spark Streaming and Flume, and how they integrate with each other.]]>
This is the talk I gave at the Big Data Meetup in Seattle in March. In this talk, I discuss the fundamentals of Spark Streaming and Flume, and how they integrate with each other.]]> Thu, 12 Mar 2015 20:56:41 GMT /slideshow/sparkflume-seattle/45780021 harisr1234@slideshare.net(harisr1234) Spark+flume seattle harisr1234 This is the talk I gave at the Big Data Meetup in Seattle in March. In this talk, I discuss the fundamentals of Spark Streaming and Flume, and how they integrate with each other. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/sparkflumeseattle-150312205641-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> This is the talk I gave at the Big Data Meetup in Seattle in March. In this talk, I discuss the fundamentals of Spark Streaming and Flume, and how they integrate with each other.

Spark+flume seattle from Hari Shreedharan

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0 Real Time Data Processing Using Spark Streaming /slideshow/spark-seattle/45779947 sparkseattle-150312205244-conversion-gate01
This is the talk I gave at the Seattle Spark Meetup in March, 2015. I discussed some Spark Streaming fundamentals, integration points with Kafka, Flume etc.]]>
This is the talk I gave at the Seattle Spark Meetup in March, 2015. I discussed some Spark Streaming fundamentals, integration points with Kafka, Flume etc.]]> Thu, 12 Mar 2015 20:52:44 GMT /slideshow/spark-seattle/45779947 harisr1234@slideshare.net(harisr1234) Real Time Data Processing Using Spark Streaming harisr1234 This is the talk I gave at the Seattle Spark Meetup in March, 2015. I discussed some Spark Streaming fundamentals, integration points with Kafka, Flume etc. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/sparkseattle-150312205244-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> This is the talk I gave at the Seattle Spark Meetup in March, 2015. I discussed some Spark Streaming fundamentals, integration points with Kafka, Flume etc.

Real Time Data Processing Using Spark Streaming from Hari Shreedharan

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0 Real Time Data Processing Using Spark Streaming /slideshow/real-time-data-processing-using-spark-streaming/43392776 sparkstreamingdatadaytexas-150110212353-conversion-gate01
Apache Spark has emerged over the past year as the imminent successor to Hadoop MapReduce. Spark can process data in memory at very high speed, while still be able to spill to disk if required. Spark’s powerful, yet flexible API allows users to write complex applications very easily without worrying about the internal workings and how the data gets processed on the cluster. Spark comes with an extremely powerful Streaming API to process data as it is ingested. Spark Streaming integrates with popular data ingest systems like Apache Flume, Apache Kafka, Amazon Kinesis etc. allowing users to process data as it comes in. In this talk, Hari will discuss the basics of Spark Streaming, its API and its integration with Flume, Kafka and Kinesis. Hari will also discuss a real-world example of a Spark Streaming application, and how code can be shared between a Spark application and a Spark Streaming application. Each stage of the application execution will be presented, which can help understand practices while writing such an application. Hari will finally discuss how to write a custom application and a custom receiver to receive data from other systems.]]>
Apache Spark has emerged over the past year as the imminent successor to Hadoop MapReduce. Spark can process data in memory at very high speed, while still be able to spill to disk if required. Spark’s powerful, yet flexible API allows users to write complex applications very easily without worrying about the internal workings and how the data gets processed on the cluster. Spark comes with an extremely powerful Streaming API to process data as it is ingested. Spark Streaming integrates with popular data ingest systems like Apache Flume, Apache Kafka, Amazon Kinesis etc. allowing users to process data as it comes in. In this talk, Hari will discuss the basics of Spark Streaming, its API and its integration with Flume, Kafka and Kinesis. Hari will also discuss a real-world example of a Spark Streaming application, and how code can be shared between a Spark application and a Spark Streaming application. Each stage of the application execution will be presented, which can help understand practices while writing such an application. Hari will finally discuss how to write a custom application and a custom receiver to receive data from other systems.]]> Sat, 10 Jan 2015 21:23:53 GMT /slideshow/real-time-data-processing-using-spark-streaming/43392776 harisr1234@slideshare.net(harisr1234) Real Time Data Processing Using Spark Streaming harisr1234 Apache Spark has emerged over the past year as the imminent successor to Hadoop MapReduce. Spark can process data in memory at very high speed, while still be able to spill to disk if required. Spark’s powerful, yet flexible API allows users to write complex applications very easily without worrying about the internal workings and how the data gets processed on the cluster. Spark comes with an extremely powerful Streaming API to process data as it is ingested. Spark Streaming integrates with popular data ingest systems like Apache Flume, Apache Kafka, Amazon Kinesis etc. allowing users to process data as it comes in. In this talk, Hari will discuss the basics of Spark Streaming, its API and its integration with Flume, Kafka and Kinesis. Hari will also discuss a real-world example of a Spark Streaming application, and how code can be shared between a Spark application and a Spark Streaming application. Each stage of the application execution will be presented, which can help understand practices while writing such an application. Hari will finally discuss how to write a custom application and a custom receiver to receive data from other systems. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/sparkstreamingdatadaytexas-150110212353-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Apache Spark has emerged over the past year as the imminent successor to Hadoop MapReduce. Spark can process data in memory at very high speed, while still be able to spill to disk if required. Spark’s powerful, yet flexible API allows users to write complex applications very easily without worrying about the internal workings and how the data gets processed on the cluster. Spark comes with an extremely powerful Streaming API to process data as it is ingested. Spark Streaming integrates with popular data ingest systems like Apache Flume, Apache Kafka, Amazon Kinesis etc. allowing users to process data as it comes in. In this talk, Hari will discuss the basics of Spark Streaming, its API and its integration with Flume, Kafka and Kinesis. Hari will also discuss a real-world example of a Spark Streaming application, and how code can be shared between a Spark application and a Spark Streaming application. Each stage of the application execution will be presented, which can help understand practices while writing such an application. Hari will finally discuss how to write a custom application and a custom receiver to receive data from other systems.

Real Time Data Processing Using Spark Streaming from Hari Shreedharan

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0 https://public.slidesharecdn.com/v2/images/profile-picture.png Building large scale distributed systems is a passion of mine. My goal is to build scalable, fault tolerant distributed systems. Specialties: Distributed systems, large scale computing, high performance systems. http://www.cs.cornell.edu/~hs465 https://cdn.slidesharecdn.com/ss_thumbnails/streamsetsandspark-retail-170608001015-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/streamsets-and-spark-in-retail/76747581 Streamsets and spark i... https://cdn.slidesharecdn.com/ss_thumbnails/streamsetsandsparksfhug-170608000826-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/streamsets-and-spark-at-sf-hadoop-user-group/76747542 Streamsets and spark a... https://cdn.slidesharecdn.com/ss_thumbnails/streamsetsandspark-170303063730-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/streamsets-and-spark-72763672/72763672 Streamsets and spark