狠狠撸shows by User: jwinandy / http://www.slideshare.net/images/logo.gif 狠狠撸shows by User: jwinandy / Mon, 04 Mar 2019 15:58:14 GMT 狠狠撸Share feed for 狠狠撸shows by User: jwinandy Scala pour le Data Eng https://fr.slideshare.net/slideshow/scala-pour-le-data-eng/134475193 scalafordataeng-190304155814
Pourquoi on utilise Scala pour le Data Eng.]]>

Pourquoi on utilise Scala pour le Data Eng.]]>
Mon, 04 Mar 2019 15:58:14 GMT https://fr.slideshare.net/slideshow/scala-pour-le-data-eng/134475193 jwinandy@slideshare.net(jwinandy) Scala pour le Data Eng jwinandy Pourquoi on utilise Scala pour le Data Eng. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/scalafordataeng-190304155814-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Pourquoi on utilise Scala pour le Data Eng.
from univalence
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Spark-adabra, Comment Construire un DATALAKE ! (Devoxx 2017) https://fr.slideshare.net/slideshow/sparkadabra-comment-construire-un-datalake-devoxx-2017/75353465 devoxx2017-170424145724
Tallk pr茅sent茅 脿 Devoxx avec Bachir Ait M'Barek : https://www.linkedin.com/in/baitmbarek C鈥檈st la r茅volution dans la BI, les zones tampon FTP laissent la place aux syst猫mes de fichier distribu茅s, le SQL s'ex茅cute sur Hadoop, les dashboard en HTML5 remplacent les clients lourds, mais ne peut-on pas rationaliser un peu l鈥檃pproche ? Comment s鈥檡 prendre pour transformer une chaine BI en datalake ? Cette universit茅 fera le tour de l鈥檌ng茅nierie des donn茅es en mode BigData. Au travers d鈥檜ne pr茅sentation d茅taill茅e des concepts, de retour d鈥檈xp茅riences et d鈥檜n cas pratique, nous allons d茅couvrir : les technologies et l鈥檃rchitecture, avec Spark, Kafka, Elasticsearch, Impala et Mesos, et les m茅thodes associ茅es : cycle de d茅veloppement avec Hadoop, tests unitaires, jointures, gestion de la qualit茅 de donn茅e, recette en mode Big Data et gestion des m茅tadonn茅es.]]>

Tallk pr茅sent茅 脿 Devoxx avec Bachir Ait M'Barek : https://www.linkedin.com/in/baitmbarek C鈥檈st la r茅volution dans la BI, les zones tampon FTP laissent la place aux syst猫mes de fichier distribu茅s, le SQL s'ex茅cute sur Hadoop, les dashboard en HTML5 remplacent les clients lourds, mais ne peut-on pas rationaliser un peu l鈥檃pproche ? Comment s鈥檡 prendre pour transformer une chaine BI en datalake ? Cette universit茅 fera le tour de l鈥檌ng茅nierie des donn茅es en mode BigData. Au travers d鈥檜ne pr茅sentation d茅taill茅e des concepts, de retour d鈥檈xp茅riences et d鈥檜n cas pratique, nous allons d茅couvrir : les technologies et l鈥檃rchitecture, avec Spark, Kafka, Elasticsearch, Impala et Mesos, et les m茅thodes associ茅es : cycle de d茅veloppement avec Hadoop, tests unitaires, jointures, gestion de la qualit茅 de donn茅e, recette en mode Big Data et gestion des m茅tadonn茅es.]]>
Mon, 24 Apr 2017 14:57:24 GMT https://fr.slideshare.net/slideshow/sparkadabra-comment-construire-un-datalake-devoxx-2017/75353465 jwinandy@slideshare.net(jwinandy) Spark-adabra, Comment Construire un DATALAKE ! (Devoxx 2017) jwinandy Tallk pr茅sent茅 脿 Devoxx avec Bachir Ait M'Barek : https://www.linkedin.com/in/baitmbarek C鈥檈st la r茅volution dans la BI, les zones tampon FTP laissent la place aux syst猫mes de fichier distribu茅s, le SQL s'ex茅cute sur Hadoop, les dashboard en HTML5 remplacent les clients lourds, mais ne peut-on pas rationaliser un peu l鈥檃pproche ? Comment s鈥檡 prendre pour transformer une chaine BI en datalake ? Cette universit茅 fera le tour de l鈥檌ng茅nierie des donn茅es en mode BigData. Au travers d鈥檜ne pr茅sentation d茅taill茅e des concepts, de retour d鈥檈xp茅riences et d鈥檜n cas pratique, nous allons d茅couvrir : les technologies et l鈥檃rchitecture, avec Spark, Kafka, Elasticsearch, Impala et Mesos, et les m茅thodes associ茅es : cycle de d茅veloppement avec Hadoop, tests unitaires, jointures, gestion de la qualit茅 de donn茅e, recette en mode Big Data et gestion des m茅tadonn茅es. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/devoxx2017-170424145724-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Tallk pr茅sent茅 脿 Devoxx avec Bachir Ait M&#39;Barek : https://www.linkedin.com/in/baitmbarek C鈥檈st la r茅volution dans la BI, les zones tampon FTP laissent la place aux syst猫mes de fichier distribu茅s, le SQL s&#39;ex茅cute sur Hadoop, les dashboard en HTML5 remplacent les clients lourds, mais ne peut-on pas rationaliser un peu l鈥檃pproche ? Comment s鈥檡 prendre pour transformer une chaine BI en datalake ? Cette universit茅 fera le tour de l鈥檌ng茅nierie des donn茅es en mode BigData. Au travers d鈥檜ne pr茅sentation d茅taill茅e des concepts, de retour d鈥檈xp茅riences et d鈥檜n cas pratique, nous allons d茅couvrir : les technologies et l鈥檃rchitecture, avec Spark, Kafka, Elasticsearch, Impala et Mesos, et les m茅thodes associ茅es : cycle de d茅veloppement avec Hadoop, tests unitaires, jointures, gestion de la qualit茅 de donn茅e, recette en mode Big Data et gestion des m茅tadonn茅es.
from univalence
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7 key recipes for data engineering /slideshow/7-key-recipes-for-data-engineering-73655793/73655793 jpscalamatsuri-7keyrecipesfordataengineering2-170326145607
Talk for Scala Matsuri (Tokyo/Japan) http://2017.scalamatsuri.org/index_en.html#schedule]]>

Talk for Scala Matsuri (Tokyo/Japan) http://2017.scalamatsuri.org/index_en.html#schedule]]>
Sun, 26 Mar 2017 14:56:07 GMT /slideshow/7-key-recipes-for-data-engineering-73655793/73655793 jwinandy@slideshare.net(jwinandy) 7 key recipes for data engineering jwinandy Talk for Scala Matsuri (Tokyo/Japan) http://2017.scalamatsuri.org/index_en.html#schedule <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/jpscalamatsuri-7keyrecipesfordataengineering2-170326145607-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Talk for Scala Matsuri (Tokyo/Japan) http://2017.scalamatsuri.org/index_en.html#schedule
7 key recipes for data engineering from univalence
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7 key recipes for data engineering /slideshow/7-key-recipes-for-data-engineering/70406145 7keyrecipesfor0bdataengineering-161223164715
After the construction of several datalakes and large business intelligence pipelines, we now know that the use of Scala and its principles were essential to the success of those large undertakings. In this talk, we will go through the 7 key scala-based architectures and methodologies that were used in real-life projects. More specifically, we will see the impact of these recipes on Spark performances, and how it enabled the rapid growth of those projects.]]>

After the construction of several datalakes and large business intelligence pipelines, we now know that the use of Scala and its principles were essential to the success of those large undertakings. In this talk, we will go through the 7 key scala-based architectures and methodologies that were used in real-life projects. More specifically, we will see the impact of these recipes on Spark performances, and how it enabled the rapid growth of those projects.]]>
Fri, 23 Dec 2016 16:47:14 GMT /slideshow/7-key-recipes-for-data-engineering/70406145 jwinandy@slideshare.net(jwinandy) 7 key recipes for data engineering jwinandy After the construction of several datalakes and large business intelligence pipelines, we now know that the use of Scala and its principles were essential to the success of those large undertakings. In this talk, we will go through the 7 key scala-based architectures and methodologies that were used in real-life projects. More specifically, we will see the impact of these recipes on Spark performances, and how it enabled the rapid growth of those projects. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/7keyrecipesfor0bdataengineering-161223164715-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> After the construction of several datalakes and large business intelligence pipelines, we now know that the use of Scala and its principles were essential to the success of those large undertakings. In this talk, we will go through the 7 key scala-based architectures and methodologies that were used in real-life projects. More specifically, we will see the impact of these recipes on Spark performances, and how it enabled the rapid growth of those projects.
7 key recipes for data engineering from univalence
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Streaming in Scala with Avro /slideshow/streaming-in-scala-with-avro/61391697 psug-160426213601
June 2015 slidedeck]]>

June 2015 slidedeck]]>
Tue, 26 Apr 2016 21:36:01 GMT /slideshow/streaming-in-scala-with-avro/61391697 jwinandy@slideshare.net(jwinandy) Streaming in Scala with Avro jwinandy June 2015 slidedeck <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/psug-160426213601-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> June 2015 slidedeck
Streaming in Scala with Avro from univalence
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Beyond tabular data /slideshow/beyond-tabular-data/52819200 beyondtabulardata-150915195256-lva1-app6892
Talk @ Paris Data Geek on Data Tables and nested data.]]>

Talk @ Paris Data Geek on Data Tables and nested data.]]>
Tue, 15 Sep 2015 19:52:56 GMT /slideshow/beyond-tabular-data/52819200 jwinandy@slideshare.net(jwinandy) Beyond tabular data jwinandy Talk @ Paris Data Geek on Data Tables and nested data. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/beyondtabulardata-150915195256-lva1-app6892-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Talk @ Paris Data Geek on Data Tables and nested data.
Beyond tabular data from univalence
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Introduction 脿 kafka /jwinandy/intro-kafka introkafka-150318102020-conversion-gate01
Petit introduction 脿 Kafka, la video est ici : https://www.youtube.com/watch?v=amus0U-dmJU]]>

Petit introduction 脿 Kafka, la video est ici : https://www.youtube.com/watch?v=amus0U-dmJU]]>
Wed, 18 Mar 2015 10:20:20 GMT /jwinandy/intro-kafka jwinandy@slideshare.net(jwinandy) Introduction 脿 kafka jwinandy Petit introduction 脿 Kafka, la video est ici : https://www.youtube.com/watch?v=amus0U-dmJU <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/introkafka-150318102020-conversion-gate01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Petit introduction 脿 Kafka, la video est ici : https://www.youtube.com/watch?v=amus0U-dmJU
Introduction 脿 kafka from univalence
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Data encoding and Metadata for Streams /jwinandy/data-encoding-and-metadata-for-streams stream-141031061225-conversion-gate01
The slides for the talk I gave at the OpenWorld Forum. It's applicable in the context of event-sourcing or Real-Time Streams (Kafka).]]>

The slides for the talk I gave at the OpenWorld Forum. It's applicable in the context of event-sourcing or Real-Time Streams (Kafka).]]>
Fri, 31 Oct 2014 06:12:25 GMT /jwinandy/data-encoding-and-metadata-for-streams jwinandy@slideshare.net(jwinandy) Data encoding and Metadata for Streams jwinandy The slides for the talk I gave at the OpenWorld Forum. It's applicable in the context of event-sourcing or Real-Time Streams (Kafka). <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/stream-141031061225-conversion-gate01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The slides for the talk I gave at the OpenWorld Forum. It&#39;s applicable in the context of event-sourcing or Real-Time Streams (Kafka).
Data encoding and Metadata for Streams from univalence
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Introduction aux Macros /slideshow/presentation-macro/30528312 presentationmacro-140128051712-phpapp02
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Tue, 28 Jan 2014 05:17:12 GMT /slideshow/presentation-macro/30528312 jwinandy@slideshare.net(jwinandy) Introduction aux Macros jwinandy <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/presentationmacro-140128051712-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br>
Introduction aux Macros from univalence
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Big data forever /slideshow/big-data-forever-16483608/16483608 bigdataforever-130212031533-phpapp01
My talk at Paris Data meetup.]]>

My talk at Paris Data meetup.]]>
Tue, 12 Feb 2013 03:15:33 GMT /slideshow/big-data-forever-16483608/16483608 jwinandy@slideshare.net(jwinandy) Big data forever jwinandy My talk at Paris Data meetup. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/bigdataforever-130212031533-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> My talk at Paris Data meetup.
Big data forever from univalence
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https://cdn.slidesharecdn.com/profile-photo-jwinandy-48x48.jpg?cb=1708692435 Consulting Data Engineer. We are engineers specialised in data management with Spark, Hadoop and Kafka. We offer our consulting and training services across Europe to small and medium companies that aspire to build solid foundations for their data. We are available for remote or on-site consulting. www.univalence.io https://cdn.slidesharecdn.com/ss_thumbnails/scalafordataeng-190304155814-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/scala-pour-le-data-eng/134475193 Scala pour le Data Eng https://cdn.slidesharecdn.com/ss_thumbnails/devoxx2017-170424145724-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/sparkadabra-comment-construire-un-datalake-devoxx-2017/75353465 Spark-adabra, Comment ... https://cdn.slidesharecdn.com/ss_thumbnails/jpscalamatsuri-7keyrecipesfordataengineering2-170326145607-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/7-key-recipes-for-data-engineering-73655793/73655793 7 key recipes for data...