Archives de Catégorie: HADOOP

Une promesse est une promesse

Bonjour,

Cela fait longtemps que ne n’ai pas alimenté ces lignes. J’en suis désolé. C’est donc avec pas mal de matière que je reviens vers vous. De plus, j’ai promis cette publication, dont acte. Les deux présentations ci dessous dressent le paysage de l’offre éditeurs dans les domaines concernés.

No SQL 10082015 slide

Hadoop 23102015 slide

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Classé dans DATA, HADOOP

Big data startup Databricks is now certifying applications for Spark

Spark was created as a processing framework for Hadoop that’s both faster and easier to use than the traditional MapReduce framework, and it’s catching on fast among folks writing big data applications.

Gigaom

Databricks , a new startup dedicated to commercializing the Apache Spark data-processing framework , has launched a « Certified on Spark » program for software vendors that want to tout their abilities to run on the increasingly popular technology. Spark was created as a processing framework for Hadoop that’s both faster and easier to use than the traditional MapReduce framework, and it’s catching on fast among folks writing big data applications.

Spark’s popularity is based on a few factors, including that it supports numerous programming languages (all of which are easier to write in than MapReduce) and supports faster data analysis both in-memory and on disk. It also allows for iterative queries on existing datasets, which — along with its speed — makes it more ideal for machine learning workloads. There are a number of workload-specific implementations on top of Spark, too, including Shark for interactive SQL queries, SparkR for statistical…

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MapR now supports YARN, puts HP Vertica on top of Hadoop

Gigaom

MapR is stepping up the feature set of its Hadoop software, announcing on Tuesday the addition of support for the YARN resource manager and the ability to run HP’s Vertica analytics software directly atop the MapR file system. The latter feature, in particular, is emblematic of MapR’s approach to keeping up with — or even passing in some areas — Hadoop mindshare (and presumably marketshare) leaders Cloudera and Hortonworks.

The addition of YARN support is important, but also something MapR had to do eventually. YARN is the resource management technology that became part of Apache Hadoop with its 2.0 release in 2012 (and which was just granted general availability status in 2013). YARN lets multiple computing frameworks run on the same Hadoop cluster using the same underlying storage. So, for example, a company could process data using MapReduce, a graph processing engine, Spark and MapR’s Drill SQL-on-Hadoop technology

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Gigaom Research webinar: the business case for Hadoop

Gigaom

As Hadoop moves from the early-adopter phase into the mainstream, IT organizations across all industries are asking how to make the business case for Hadoop at their company. For all of its benefits, namely it is a radically cheaper alternative for storing and processing large amounts of structured and unstructured data, and it can be an uncomfortable fit in a traditional IT environment. With that in mind, it’s important to put Hadoop to work on the tasks it was designed for, to ensure its success in your organization.

In this webinar, our panel will discuss these topics:

  • What evidence is there that Hadoop is now mainstream?
  • What use cases is Hadoop most suited to?
  • What are the challenges and best practices for implementing Hadoop in an ETL offload environment?
  • What are some methodologies and tools for success?

Speakers include:

  • David Loshin, principal consultant, corporate analyst and…

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Faut il bruler l entrepot de donnees traditionnel?

Voici une lecture que je vous soumets pour une raison évidente que je vous laisse découvrir; ENJOY:
http://www.decideo.fr/Faut-il-bruler-l-entrepot-de-donnees-traditionnel_a6503.html

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Classé dans DATA, ETL, HADOOP

Pourquoi ne pas utiliser les outils traditionnels du décisionnel qui, dans certains, seront tout à fait adaptés ?
LIRE L’ARTICLE ICI

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octobre 15, 2013 · 1:12

Hadoop, c’est quoi? Comment ca marche?

Je me suis posé cette question, avant l’été, et ai pris le temps de gratter afin de savoir exactement quels types de technologies étaient mis en oeuvre derrière le Big Data.

L’essenciel des réponses est dans le document dessous:

https://aprevotleygonie.files.wordpress.com/2013/10/hadoopv2.pdf

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Classé dans DATA, HADOOP