�ݺ�ߣshows by User: MartinZapletal

�ݺ�ߣshows by User: MartinZapletal / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: MartinZapletal / Mon, 31 May 2021 21:20:11 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: MartinZapletal How Disney+ uses fast data ubiquity to improve the customer experience /slideshow/how-disney-uses-fast-data-ubiquity-to-improve-the-customer-experience/248803034 finalant309pattonreivant309howdisneyusesdataubiquity202010212wdedited-210531212011
Disney+ uses Amazon Kinesis to drive real-time actions like providing title recommendations for customers, sending events across microservices, and delivering logs for operational analytics to improve the customer experience. In this session, you learn how Disney+ built real-time data-driven capabilities on a unified streaming platform. This platform ingests billions of events per hour in Amazon Kinesis Data Streams, processes and analyzes that data in Amazon Kinesis Data Analytics for Apache Flink, and uses Amazon Kinesis Data Firehose to deliver data to destinations without servers or code. Hear how these services helped Disney+ scale its viewing experience to tens of millions of customers with the required quality and reliability. Learn more about re:Invent 2020 at http://bit.ly/3c4NSdY ]]>
Disney+ uses Amazon Kinesis to drive real-time actions like providing title recommendations for customers, sending events across microservices, and delivering logs for operational analytics to improve the customer experience. In this session, you learn how Disney+ built real-time data-driven capabilities on a unified streaming platform. This platform ingests billions of events per hour in Amazon Kinesis Data Streams, processes and analyzes that data in Amazon Kinesis Data Analytics for Apache Flink, and uses Amazon Kinesis Data Firehose to deliver data to destinations without servers or code. Hear how these services helped Disney+ scale its viewing experience to tens of millions of customers with the required quality and reliability. Learn more about re:Invent 2020 at http://bit.ly/3c4NSdY ]]> Mon, 31 May 2021 21:20:11 GMT /slideshow/how-disney-uses-fast-data-ubiquity-to-improve-the-customer-experience/248803034 MartinZapletal@slideshare.net(MartinZapletal) How Disney+ uses fast data ubiquity to improve the customer experience MartinZapletal Disney+ uses Amazon Kinesis to drive real-time actions like providing title recommendations for customers, sending events across microservices, and delivering logs for operational analytics to improve the customer experience. In this session, you learn how Disney+ built real-time data-driven capabilities on a unified streaming platform. This platform ingests billions of events per hour in Amazon Kinesis Data Streams, processes and analyzes that data in Amazon Kinesis Data Analytics for Apache Flink, and uses Amazon Kinesis Data Firehose to deliver data to destinations without servers or code. Hear how these services helped Disney+ scale its viewing experience to tens of millions of customers with the required quality and reliability. Learn more about re:Invent 2020 at http://bit.ly/3c4NSdY <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/finalant309pattonreivant309howdisneyusesdataubiquity202010212wdedited-210531212011-thumbnail.jpg?width=120&height=120&fit=bounds" /> Disney+ uses Amazon Kinesis to drive real-time actions like providing title recommendations for customers, sending events across microservices, and delivering logs for operational analytics to improve the customer experience. In this session, you learn how Disney+ built real-time data-driven capabilities on a unified streaming platform. This platform ingests billions of events per hour in Amazon Kinesis Data Streams, processes and analyzes that data in Amazon Kinesis Data Analytics for Apache Flink, and uses Amazon Kinesis Data Firehose to deliver data to destinations without servers or code. Hear how these services helped Disney+ scale its viewing experience to tens of millions of customers with the required quality and reliability. Learn more about re:Invent 2020 at http://bit.ly/3c4NSdY

How Disney+ uses fast data ubiquity to improve the customer experience from Martin Zapletal

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0 Customer experience at disney+ through data perspective /slideshow/customer-experience-at-disney-through-data-perspective/248802857 customerexperienceatdisneythroughdataperspective-210531211245
Disney+ has rapidly scaled to provide a personalized and seamless experience to tens of millions of customers. This experience is powered by a robust data platform that ingests, processes and surfaces billions of events per hour using Delta lake, Databricks, and AWS technologies. The data produced by the platform is used by multitude of services including a recommendation engine for personalized experience, optimizing watch experience including group watch, and fraud and abuse prevention. In this session, you will learn how Disney+ built these capabilities, the architecture, technologies, design principles, and technical details that make it possible.]]>
Disney+ has rapidly scaled to provide a personalized and seamless experience to tens of millions of customers. This experience is powered by a robust data platform that ingests, processes and surfaces billions of events per hour using Delta lake, Databricks, and AWS technologies. The data produced by the platform is used by multitude of services including a recommendation engine for personalized experience, optimizing watch experience including group watch, and fraud and abuse prevention. In this session, you will learn how Disney+ built these capabilities, the architecture, technologies, design principles, and technical details that make it possible.]]> Mon, 31 May 2021 21:12:45 GMT /slideshow/customer-experience-at-disney-through-data-perspective/248802857 MartinZapletal@slideshare.net(MartinZapletal) Customer experience at disney+ through data perspective MartinZapletal Disney+ has rapidly scaled to provide a personalized and seamless experience to tens of millions of customers. This experience is powered by a robust data platform that ingests, processes and surfaces billions of events per hour using Delta lake, Databricks, and AWS technologies. The data produced by the platform is used by multitude of services including a recommendation engine for personalized experience, optimizing watch experience including group watch, and fraud and abuse prevention. In this session, you will learn how Disney+ built these capabilities, the architecture, technologies, design principles, and technical details that make it possible. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/customerexperienceatdisneythroughdataperspective-210531211245-thumbnail.jpg?width=120&height=120&fit=bounds" /> Disney+ has rapidly scaled to provide a personalized and seamless experience to tens of millions of customers. This experience is powered by a robust data platform that ingests, processes and surfaces billions of events per hour using Delta lake, Databricks, and AWS technologies. The data produced by the platform is used by multitude of services including a recommendation engine for personalized experience, optimizing watch experience including group watch, and fraud and abuse prevention. In this session, you will learn how Disney+ built these capabilities, the architecture, technologies, design principles, and technical details that make it possible.

Customer experience at disney+ through data perspective from Martin Zapletal

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0 Intelligent System Optimizations /slideshow/intelligent-system-optimizations/82245267 scalebythebay2017-171117230403
Using observability, logs, metrics and traces as a data source for supervised and reinforcement machine learning techniques with a goal to optimize large scale systems.]]>
Using observability, logs, metrics and traces as a data source for supervised and reinforcement machine learning techniques with a goal to optimize large scale systems.]]> Fri, 17 Nov 2017 23:04:03 GMT /slideshow/intelligent-system-optimizations/82245267 MartinZapletal@slideshare.net(MartinZapletal) Intelligent System Optimizations MartinZapletal Using observability, logs, metrics and traces as a data source for supervised and reinforcement machine learning techniques with a goal to optimize large scale systems. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/scalebythebay2017-171117230403-thumbnail.jpg?width=120&height=120&fit=bounds" /> Using observability, logs, metrics and traces as a data source for supervised and reinforcement machine learning techniques with a goal to optimize large scale systems.

Intelligent System Optimizations from Martin Zapletal

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0 Intelligent Distributed Systems Optimizations /slideshow/intelligent-distributed-systems-optimizations/80991091 zapletalmartinreactivesummit2017-171019171806
This talk discusses techniques for achieving optimized performance, availability, cost or other attributes of a distributed system. Firstly, the presentation introduces and in depth explains optimization techniques used in state of the art large scale stream and fast data processing frameworks such as Akka Streams, Spark or Flink, including logical and physical optimizations or code generation. Consequently, powerful optimization concepts applicable to general distributed systems, including systems built using Akka, are explained on examples. Finally, the presentation highlights the role of machine learning and artificial intelligence in the area and explains how machine generated data such as logs and metrics can be used to model, minimize, maximize or find the perfect balance of selected attributes of the system, demonstrated on examples from practice. The attendees will gain an understanding of the available optimization approaches, tradeoffs and the value of machine learning and intelligence and ultimately will be able to apply some of the techniques to optimize general distributed systems as well as streaming data processing systems built using Spark, Flink or Akka Streams.]]>
This talk discusses techniques for achieving optimized performance, availability, cost or other attributes of a distributed system. Firstly, the presentation introduces and in depth explains optimization techniques used in state of the art large scale stream and fast data processing frameworks such as Akka Streams, Spark or Flink, including logical and physical optimizations or code generation. Consequently, powerful optimization concepts applicable to general distributed systems, including systems built using Akka, are explained on examples. Finally, the presentation highlights the role of machine learning and artificial intelligence in the area and explains how machine generated data such as logs and metrics can be used to model, minimize, maximize or find the perfect balance of selected attributes of the system, demonstrated on examples from practice. The attendees will gain an understanding of the available optimization approaches, tradeoffs and the value of machine learning and intelligence and ultimately will be able to apply some of the techniques to optimize general distributed systems as well as streaming data processing systems built using Spark, Flink or Akka Streams.]]> Thu, 19 Oct 2017 17:18:06 GMT /slideshow/intelligent-distributed-systems-optimizations/80991091 MartinZapletal@slideshare.net(MartinZapletal) Intelligent Distributed Systems Optimizations MartinZapletal This talk discusses techniques for achieving optimized performance, availability, cost or other attributes of a distributed system. Firstly, the presentation introduces and in depth explains optimization techniques used in state of the art large scale stream and fast data processing frameworks such as Akka Streams, Spark or Flink, including logical and physical optimizations or code generation. Consequently, powerful optimization concepts applicable to general distributed systems, including systems built using Akka, are explained on examples. Finally, the presentation highlights the role of machine learning and artificial intelligence in the area and explains how machine generated data such as logs and metrics can be used to model, minimize, maximize or find the perfect balance of selected attributes of the system, demonstrated on examples from practice. The attendees will gain an understanding of the available optimization approaches, tradeoffs and the value of machine learning and intelligence and ultimately will be able to apply some of the techniques to optimize general distributed systems as well as streaming data processing systems built using Spark, Flink or Akka Streams. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/zapletalmartinreactivesummit2017-171019171806-thumbnail.jpg?width=120&height=120&fit=bounds" /> This talk discusses techniques for achieving optimized performance, availability, cost or other attributes of a distributed system. Firstly, the presentation introduces and in depth explains optimization techniques used in state of the art large scale stream and fast data processing frameworks such as Akka Streams, Spark or Flink, including logical and physical optimizations or code generation. Consequently, powerful optimization concepts applicable to general distributed systems, including systems built using Akka, are explained on examples. Finally, the presentation highlights the role of machine learning and artificial intelligence in the area and explains how machine generated data such as logs and metrics can be used to model, minimize, maximize or find the perfect balance of selected attributes of the system, demonstrated on examples from practice. The attendees will gain an understanding of the available optimization approaches, tradeoffs and the value of machine learning and intelligence and ultimately will be able to apply some of the techniques to optimize general distributed systems as well as streaming data processing systems built using Spark, Flink or Akka Streams.

Intelligent Distributed Systems Optimizations from Martin Zapletal

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0 Data in Motion: Streaming Static Data Efficiently 2 /slideshow/data-in-motion-streaming-static-data-efficiently-2/63171388 martinzapletaldatinmotion-160617110447
Updated version for SD Berlin 2016. Distributed streaming performance, consistency, reliable delivery, durability, optimisations, event time processing and other concepts discussed and explained on Akka Persistence and other examples.]]>
Updated version for SD Berlin 2016. Distributed streaming performance, consistency, reliable delivery, durability, optimisations, event time processing and other concepts discussed and explained on Akka Persistence and other examples.]]> Fri, 17 Jun 2016 11:04:47 GMT /slideshow/data-in-motion-streaming-static-data-efficiently-2/63171388 MartinZapletal@slideshare.net(MartinZapletal) Data in Motion: Streaming Static Data Efficiently 2 MartinZapletal Updated version for SD Berlin 2016. Distributed streaming performance, consistency, reliable delivery, durability, optimisations, event time processing and other concepts discussed and explained on Akka Persistence and other examples. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/martinzapletaldatinmotion-160617110447-thumbnail.jpg?width=120&height=120&fit=bounds" /> Updated version for SD Berlin 2016. Distributed streaming performance, consistency, reliable delivery, durability, optimisations, event time processing and other concepts discussed and explained on Akka Persistence and other examples.

Data in Motion: Streaming Static Data Efficiently 2 from Martin Zapletal

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0 Data in Motion: Streaming Static Data Efficiently /slideshow/data-in-motion-streaming-static-data-efficiently/61916622 zapletalmartindatainmotionstreamingstaticdataefficiently-160511174857
Distributed streaming performance, consistency, reliable delivery, durability, optimisations, event time processing and other concepts discussed and explained on Akka Persistence and other examples.]]>
Distributed streaming performance, consistency, reliable delivery, durability, optimisations, event time processing and other concepts discussed and explained on Akka Persistence and other examples.]]> Wed, 11 May 2016 17:48:57 GMT /slideshow/data-in-motion-streaming-static-data-efficiently/61916622 MartinZapletal@slideshare.net(MartinZapletal) Data in Motion: Streaming Static Data Efficiently MartinZapletal Distributed streaming performance, consistency, reliable delivery, durability, optimisations, event time processing and other concepts discussed and explained on Akka Persistence and other examples. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/zapletalmartindatainmotionstreamingstaticdataefficiently-160511174857-thumbnail.jpg?width=120&height=120&fit=bounds" /> Distributed streaming performance, consistency, reliable delivery, durability, optimisations, event time processing and other concepts discussed and explained on Akka Persistence and other examples.

Data in Motion: Streaming Static Data Efficiently from Martin Zapletal

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0 Machine learning at Scale with Apache Spark /slideshow/machine-learning-at-scale-with-apache-spark/58172400 machinelearningatscalewithapachesparkfinal-160211225906
Machine learning at scale with Apache Spark presentation by Martin Zapletal presented at Datapalooza Seattle 2016.]]>
Machine learning at scale with Apache Spark presentation by Martin Zapletal presented at Datapalooza Seattle 2016.]]> Thu, 11 Feb 2016 22:59:06 GMT /slideshow/machine-learning-at-scale-with-apache-spark/58172400 MartinZapletal@slideshare.net(MartinZapletal) Machine learning at Scale with Apache Spark MartinZapletal Machine learning at scale with Apache Spark presentation by Martin Zapletal presented at Datapalooza Seattle 2016. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/machinelearningatscalewithapachesparkfinal-160211225906-thumbnail.jpg?width=120&height=120&fit=bounds" /> Machine learning at scale with Apache Spark presentation by Martin Zapletal presented at Datapalooza Seattle 2016.

Machine learning at Scale with Apache Spark from Martin Zapletal

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0 Cassandra as an event sourced journal for big data analytics Cassandra Summit 2015 /slideshow/cassandra-as-an-event-sourced-journal-for-big-data-analytics-cassandra-summit-2015/53172174 cassandraasaneventsourcedjournalforbigdataanalyticscassandrasummit2015-150924214814-lva1-app6891
Cassandra as an event sourced journal for big data analytics Cassandra Summit 2015]]>
Cassandra as an event sourced journal for big data analytics Cassandra Summit 2015]]> Thu, 24 Sep 2015 21:48:14 GMT /slideshow/cassandra-as-an-event-sourced-journal-for-big-data-analytics-cassandra-summit-2015/53172174 MartinZapletal@slideshare.net(MartinZapletal) Cassandra as an event sourced journal for big data analytics Cassandra Summit 2015 MartinZapletal Cassandra as an event sourced journal for big data analytics Cassandra Summit 2015 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/cassandraasaneventsourcedjournalforbigdataanalyticscassandrasummit2015-150924214814-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds" /> Cassandra as an event sourced journal for big data analytics Cassandra Summit 2015

Cassandra as an event sourced journal for big data analytics Cassandra Summit 2015 from Martin Zapletal

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0 Large volume data analysis on the Typesafe Reactive Platform - Big Data Scala by the Bay 2015 /slideshow/large-volume-data-analysis-on-the-typesafe-reactive-platform-big-data-scala-by-the-bay-2015/51786572 bdsbtb-150818225625-lva1-app6892
Large volume data analysis on the Typesafe Reactive Platform - Big Data Scala by the Bay]]>
Large volume data analysis on the Typesafe Reactive Platform - Big Data Scala by the Bay]]> Tue, 18 Aug 2015 22:56:25 GMT /slideshow/large-volume-data-analysis-on-the-typesafe-reactive-platform-big-data-scala-by-the-bay-2015/51786572 MartinZapletal@slideshare.net(MartinZapletal) Large volume data analysis on the Typesafe Reactive Platform - Big Data Scala by the Bay 2015 MartinZapletal Large volume data analysis on the Typesafe Reactive Platform - Big Data Scala by the Bay <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/bdsbtb-150818225625-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" /> Large volume data analysis on the Typesafe Reactive Platform - Big Data Scala by the Bay

Large volume data analysis on the Typesafe Reactive Platform - Big Data Scala by the Bay 2015 from Martin Zapletal

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0 Large volume data analysis on the Typesafe Reactive Platform /slideshow/zapletal-martinlargevolumedataanalytics/49286837 zapletalmartinlargevolumedataanalytics-150611204218-lva1-app6891
Large volume data analysis on the Typesafe Reactive Platform. Scala Days 2015 Amsterdam slides]]>
Large volume data analysis on the Typesafe Reactive Platform. Scala Days 2015 Amsterdam slides]]> Thu, 11 Jun 2015 20:42:18 GMT /slideshow/zapletal-martinlargevolumedataanalytics/49286837 MartinZapletal@slideshare.net(MartinZapletal) Large volume data analysis on the Typesafe Reactive Platform MartinZapletal Large volume data analysis on the Typesafe Reactive Platform. Scala Days 2015 Amsterdam slides <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/zapletalmartinlargevolumedataanalytics-150611204218-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds" /> Large volume data analysis on the Typesafe Reactive Platform. Scala Days 2015 Amsterdam slides

Large volume data analysis on the Typesafe Reactive Platform from Martin Zapletal

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0 Apache spark - Installation /slideshow/apache-spark-installation/46421872 apachespark2-150329135033-conversion-gate01
Apache spark - Installation]]>
Apache spark - Installation]]> Sun, 29 Mar 2015 13:50:32 GMT /slideshow/apache-spark-installation/46421872 MartinZapletal@slideshare.net(MartinZapletal) Apache spark - Installation MartinZapletal Apache spark - Installation <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/apachespark2-150329135033-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Apache spark - Installation

Apache spark - Installation from Martin Zapletal

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0 Apache spark - Spark's distributed programming model /slideshow/apache-spark-3-46421710/46421710 ywmbjyaprb2bapj6copq-signature-3db3a17e1556848bfe53049583963bbef677077a5f04358c90161dc3d524fda3-poli-150329134145-conversion-gate01
Apache spark - Spark's distributed programming model]]>
Apache spark - Spark's distributed programming model]]> Sun, 29 Mar 2015 13:41:45 GMT /slideshow/apache-spark-3-46421710/46421710 MartinZapletal@slideshare.net(MartinZapletal) Apache spark - Spark's distributed programming model MartinZapletal Apache spark - Spark's distributed programming model <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ywmbjyaprb2bapj6copq-signature-3db3a17e1556848bfe53049583963bbef677077a5f04358c90161dc3d524fda3-poli-150329134145-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Apache spark - Spark's distributed programming model

Apache spark - Spark's distributed programming model from Martin Zapletal

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0 Apache spark - History and market overview /slideshow/apache-spark-1-46421596/46421596 5yxgrg1jrz6lxwzbgfla-signature-421b897b13ea623f41b057c1ac2699238676e8758bcde0f2856d11e5209bf267-poli-150329133648-conversion-gate01
Apache spark - History and market overview]]>
Apache spark - History and market overview]]> Sun, 29 Mar 2015 13:36:48 GMT /slideshow/apache-spark-1-46421596/46421596 MartinZapletal@slideshare.net(MartinZapletal) Apache spark - History and market overview MartinZapletal Apache spark - History and market overview <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/5yxgrg1jrz6lxwzbgfla-signature-421b897b13ea623f41b057c1ac2699238676e8758bcde0f2856d11e5209bf267-poli-150329133648-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Apache spark - History and market overview

Apache spark - History and market overview from Martin Zapletal

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0 https://cdn.slidesharecdn.com/profile-photo-MartinZapletal-48x48.jpg?cb=1622495072 https://cdn.slidesharecdn.com/ss_thumbnails/finalant309pattonreivant309howdisneyusesdataubiquity202010212wdedited-210531212011-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/how-disney-uses-fast-data-ubiquity-to-improve-the-customer-experience/248803034 How Disney+ uses fast... https://cdn.slidesharecdn.com/ss_thumbnails/customerexperienceatdisneythroughdataperspective-210531211245-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/customer-experience-at-disney-through-data-perspective/248802857 Customer experience at... https://cdn.slidesharecdn.com/ss_thumbnails/scalebythebay2017-171117230403-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/intelligent-system-optimizations/82245267 Intelligent System Opt...