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HBase. PoweredBy Hadoop. This page documents an alphabetical list of institutions that are using Hadoop for educational or production uses.

PoweredBy Hadoop

Companies that offer services on or based around Hadoop are listed in Distributions and Commercial Support . Please include details about your cluster hardware and size. Entries without this may be mistaken for spam references and deleted. To add entries you need write permission to the wiki, which you can get by subscribing to the mailing list and asking for permissions on the wiki account username you've registered yourself as.

If you are using Hadoop in production you ought to consider getting involved in the development process anyway, by filing bugs, testing beta releases, reviewing the code and turning your notes into shared documentation. Contents. HBase - HBase Home. All Things Distributed. Hadoop. PoweredBy. Big Data : comment modéliser ses données avec Hadoop-Hbase?(1/3) Traditional SQL databases, such as Oracle, DB2 and SQL server should be used for what they do best: managing a consistent and integrated transactional model.Their designs are adapted to application systems requiring a high level of consistency and guaranteed inter-table integrity.

Big Data : comment modéliser ses données avec Hadoop-Hbase?(1/3)

Consequently, they are not suitable for large queries concerning very large volumes of data.For 20 years, people have wanted to perform datawarehousing using types of databases that are not designed for that purpose. Publishers have done their best to convince users that their systems were up to the task, but it soon becomes clear that their monolithic design gives up the ghost quite quickly if there are more than a hundred million records to explore.Hadoop/Hbase: a new era.This is where the power of Hadoop/Hbase-type NoSQL and Big Data systems comes into play.These systems are designed for data distribution and processing on several machines.

Christophe. Modéliser mais comment ? Apache Mahout: Scalable machine learning and data mining. Ce que Hadoop ? Data warehouse, stockage et traitement distribués Définition Hadoop Hadoop est un projet Open Source géré par Apache Software Fundation basé sur le principe Map Reduce et de Google File System, deux produits Google Corp.

ce que Hadoop ?

Le produit est écrit en langage Java. Hadoop peut être considéré comme un système de traitement de données évolutif pour le stockage et le traitement par lot de très grande quantité de données. Il est tout à fait adapté aux stockages de grande taille et aux analyses de type "ad hoc" sur de très grandes quantité de données. Hadoop et les analyses massives Le besoin en analyse de grandes masses de données devient toujours plus pressant. Une gestion de suivi produit moderne, logistique ou tracabilité par exemple, exploitant l'identification généralisée des objets et des parcours de type RFID génère aussi des quantités incommensurables de précieuses données. Benchmark Ressources Livre recommandé. What is Hadoop? Other big data terms like MapReduce? Why Facebook Uses Apache Hadoop and HBase. Dhruba Borthakur, a Hadoop Engineer at Facebook, has published part of a paper he co-authored with several of his engineering co-workers on Apache Hadoop.

Why Facebook Uses Apache Hadoop and HBase

The first part of the paper explains Facebook's requirements and non-requirements for a data store for its revamped Facebook Messages application and the reasons it chose Apache Hadoop to power it. The paper will be published at SIGMOD 2011. The requirements: Elasticity High write throughput Efficient and low-latency strong consistency semantics within a data center Efficient random reads from disk High Availability and Disaster Recovery Fault Isolation Atomic read-modify-write primitives Range Scans The non-requirements: Tolerance of network partitions within a single data center Zero Downtime in case of individual data center failure Active-active serving capability across different data centers You can find out much by reading the paper. Image Credit: Massimo Barbieri. MapReduce. Un article de Wikipédia, l'encyclopédie libre.


Les termes « map » et « reduce », et les concepts sous-jacents, sont empruntés aux langages de programmation fonctionnelle utilisés pour leur construction (map et réduction de la programmation fonctionnelle et des langages de programmation tableau). MapReduce permet de manipuler de grandes quantités de données en les distribuant dans un cluster de machines pour être traitées. Ce modèle connaît un vif succès auprès de sociétés possédant d'importants centres de traitement de données telles Amazon ou Facebook. Il commence aussi à être utilisé au sein du Cloud computing. De nombreux frameworks ont vu le jour afin d'implémenter le MapReduce. Présentation[modifier | modifier le code]