I would be glad to join if it is possible.
On Tue, Jan 4, 2011 at 10:55 PM, Ori Lahav <ola...@gmail.com> wrote:
Apache Hadoop is an open-source program framework that supports data-intensive distributed applications, licensed under the Apache v2 license. It supports the jogging of applications on giant clusters of commodity hardware. Hadoop was derived from Google's MapReduce & Google File Technique (GFS) papers<a href="https://www.youtube.com/watch?v=PqbYn5LXzRw">Hadoop Online Training Demo in Hyderabad India</a>The Hadoop framework transparently provides both reliability & information motion to applications. Hadoop implements a computational paradigm named MapReduce, where the application is divided in to plenty of small fragments of work, each of which may be executed or re-executed on any node in the cluster. In addition, it provides a distributed file technique that stores information on the compute nodes, providing high aggregate bandwidth across the cluster. Both map/reduce & the distributed file technique are designed so that node failures are automatically handled by the framework. It allows applications to work with thousands of computation-independent computers & petabytes of information. The whole Apache Hadoop "platform" is now often thought about to consist of the Hadoop kernel, MapReduce & Hadoop Distributed File Technique (HDFS), & a variety of related projects including Apache Hive, Apache HBase, & others <a href=" http://hadooponlinetrainings.com/hadoop-online-training/">Hadoop Online Training</a>
Hadoop is written in the Java programming language & is an Apache top-level project being built & used by a worldwide community of contributors. Hadoop & its related projects (Hive, HBase, Zookeeper, & so on) have plenty of contributors from across the ecosystem. Though Java code is most common, any programming language can be used with "streaming" to implement the "map" & "reduce" parts of the technique.
Hadoop permits a computing solution that is:
Scalable New nodes can be added as needed, & added without needing to fine-tune data formats, how data is loaded, how jobs are written, or the applications on top.
Cost effective Hadoop brings massively parallel computing to commodity servers. The result is a sizeable decrease in the cost per terabyte of storage, which in turn makes it affordable to model all of your data.
Flexible Hadoop is schema-less, & can absorb any type of data, structured or not, from any number of sources. Data from multiple sources can be joined & aggregated in arbitrary ways enabling deeper analyses than any process can provide.
Fault tolerant When you lose a node, the process redirects work to another location of the data & continues processing without missing a beat.
Apache Hadoop is 100% open source, & pioneered a fundamentally new way of storing & processing data. In lieu of relying on costly, proprietary hardware & different systems to store & technique data, Hadoop permits distributed parallel processing of immense amounts of data across cheap, industry-standard servers that both store & technique the data, & can scale without limits. With Hadoop, no data is sizable. & in today's hyper-connected world where increasingly data is being created every day, Hadoop's breakthrough advantages mean that businesses & organizations can now find value in data that was recently thought about useless. <a href=" http://hadooponlinetrainings.com/hadoop-online-training/">Online Hadoop Training</a>
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Hi Guys,At FLR , we are about to launch first version of our system to production environment (US Colo DC), and I'm very interested in hearing the ones of you that uses Hadoop in production: