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Mon, 04 Mar 2019 15:58:14 GMT狠狠撸Share feed for 狠狠撸shows by User: jwinandyScala 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 GMThttps://fr.slideshare.net/slideshow/scala-pour-le-data-eng/134475193jwinandy@slideshare.net(jwinandy)Scala pour le Data EngjwinandyPourquoi 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&height=120&fit=bounds" /><br> Pourquoi on utilise Scala pour le Data Eng.
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1281https://cdn.slidesharecdn.com/ss_thumbnails/scalafordataeng-190304155814-thumbnail.jpg?width=120&height=120&fit=boundspresentationBlackhttp://activitystrea.ms/schema/1.0/posthttp://activitystrea.ms/schema/1.0/posted0Spark-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 GMThttps://fr.slideshare.net/slideshow/sparkadabra-comment-construire-un-datalake-devoxx-2017/75353465jwinandy@slideshare.net(jwinandy)Spark-adabra, Comment Construire un DATALAKE ! (Devoxx 2017) jwinandyTallk 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&height=120&fit=bounds" /><br> 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.
]]>
7822https://cdn.slidesharecdn.com/ss_thumbnails/jpscalamatsuri-7keyrecipesfordataengineering2-170326145607-thumbnail.jpg?width=120&height=120&fit=boundspresentationBlackhttp://activitystrea.ms/schema/1.0/posthttp://activitystrea.ms/schema/1.0/posted07 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/70406145jwinandy@slideshare.net(jwinandy)7 key recipes for data engineeringjwinandyAfter 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&height=120&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.
]]>
32274https://cdn.slidesharecdn.com/ss_thumbnails/7keyrecipesfor0bdataengineering-161223164715-thumbnail.jpg?width=120&height=120&fit=boundspresentationBlackhttp://activitystrea.ms/schema/1.0/posthttp://activitystrea.ms/schema/1.0/posted0Streaming 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/61391697jwinandy@slideshare.net(jwinandy)Streaming in Scala with AvrojwinandyJune 2015 slidedeck<img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/psug-160426213601-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> June 2015 slidedeck
]]>
8126https://cdn.slidesharecdn.com/ss_thumbnails/psug-160426213601-thumbnail.jpg?width=120&height=120&fit=boundspresentationBlackhttp://activitystrea.ms/schema/1.0/posthttp://activitystrea.ms/schema/1.0/posted0Beyond 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/52819200jwinandy@slideshare.net(jwinandy)Beyond tabular datajwinandyTalk @ 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&height=120&fit=bounds" /><br> Talk @ Paris Data Geek on Data Tables and nested data.
]]>
6975https://cdn.slidesharecdn.com/ss_thumbnails/beyondtabulardata-150915195256-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=boundspresentationBlackhttp://activitystrea.ms/schema/1.0/posthttp://activitystrea.ms/schema/1.0/posted0Introduction 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-kafkajwinandy@slideshare.net(jwinandy)Introduction 脿 kafkajwinandyPetit 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&height=120&fit=bounds" /><br> Petit introduction 脿 Kafka, la video est ici : https://www.youtube.com/watch?v=amus0U-dmJU
]]>
7642https://cdn.slidesharecdn.com/ss_thumbnails/introkafka-150318102020-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=boundspresentation000000http://activitystrea.ms/schema/1.0/posthttp://activitystrea.ms/schema/1.0/posted0Data 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-streamsjwinandy@slideshare.net(jwinandy)Data encoding and Metadata for StreamsjwinandyThe 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&height=120&fit=bounds" /><br> 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).
]]>
8963https://cdn.slidesharecdn.com/ss_thumbnails/presentationmacro-140128051712-phpapp02-thumbnail.jpg?width=120&height=120&fit=boundspresentation000000http://activitystrea.ms/schema/1.0/posthttp://activitystrea.ms/schema/1.0/posted0Big 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-16483608/16483608jwinandy@slideshare.net(jwinandy)Big data foreverjwinandyMy 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&height=120&fit=bounds" /><br> My talk at Paris Data meetup.
]]>
8233https://cdn.slidesharecdn.com/ss_thumbnails/bigdataforever-130212031533-phpapp01-thumbnail.jpg?width=120&height=120&fit=boundspresentationBlackhttp://activitystrea.ms/schema/1.0/posthttp://activitystrea.ms/schema/1.0/posted0https://cdn.slidesharecdn.com/profile-photo-jwinandy-48x48.jpg?cb=1708692435Consulting 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.iohttps://cdn.slidesharecdn.com/ss_thumbnails/scalafordataeng-190304155814-thumbnail.jpg?width=320&height=320&fit=boundsslideshow/scala-pour-le-data-eng/134475193Scala pour le Data Enghttps://cdn.slidesharecdn.com/ss_thumbnails/devoxx2017-170424145724-thumbnail.jpg?width=320&height=320&fit=boundsslideshow/sparkadabra-comment-construire-un-datalake-devoxx-2017/75353465Spark-adabra, Comment ...https://cdn.slidesharecdn.com/ss_thumbnails/jpscalamatsuri-7keyrecipesfordataengineering2-170326145607-thumbnail.jpg?width=320&height=320&fit=boundsslideshow/7-key-recipes-for-data-engineering-73655793/736557937 key recipes for data...