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The wiki is related to our experience on the Grid5000 and CIRRUS testbeds for the study of the Software, Platform, Infrastructure and Network layers that push forward the Data Science field according to an experimental scientific method.
 
The wiki is related to our experience on the Grid5000 and CIRRUS testbeds for the study of the Software, Platform, Infrastructure and Network layers that push forward the Data Science field according to an experimental scientific method.
  
'''General discussion on Systems for Big-Data:'''
+
'''General discussion on Systems for Big-Data'''
 
   [[Infrastructure, programming models, frameworks]]
 
   [[Infrastructure, programming models, frameworks]]
  

Version du 30 janvier 2016 à 10:54

Welcome to LIPN Wiki on Big Data

With more and more data produced every day, we need to pay a special attention on the technologies to use in order to be able to analyze large amount of data. Big Data is often characterized by the 4 V for Volume, Variety, Velocity, Veracity that constitute challenges for the required tools.

Machine learning is to extract knowledge from data. In short it's a family of algorithms that transform data into model or description with the aim to predict or categorize data. In this field we use also analytics tools consisting to present informations in a more readable way as for the Square Predict (http://square-predict.net/) project.

The wiki is related to our experience on the Grid5000 and CIRRUS testbeds for the study of the Software, Platform, Infrastructure and Network layers that push forward the Data Science field according to an experimental scientific method.

General discussion on Systems for Big-Data

  Infrastructure, programming models, frameworks

Our experience is with the following tools:

   Apache Spark : http://spark.apache.org/
   Apache Flink : https://flink.apache.org/
   TenserFlow : https://www.tensorflow.org/
   Wendelin : http://www.nexedi.com/NXD-Document.Blog.Wendelin.Release.0.4.alpha

Testbeds we use in conjunction with our experimental method:

   Grid5000 : https://www.grid5000.fr/mediawiki/index.php/Grid5000:Home
   Cirrus : http://cirrus.uspc.fr

Apache Spark

  General information on Apache Spark
  How to use Spark on Grid5000




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