Exemplos de uso de Apache hadoop em Inglês e suas traduções para o Português
{-}
-
Colloquial
-
Official
-
Medicine
-
Financial
-
Ecclesiastic
-
Ecclesiastic
-
Computer
-
Official/political
At the heart of the big data movement are Apache Hadoop* and Apache Spark.
The solution used is apache hadoop, a tool which purpose is to solve big data problems.
He also discusses how Spark compares with traditional MapReduce implementation like Apache Hadoop.
The Cubieboard team managed to run an Apache Hadoop computer cluster using the Lubuntu Linux distribution.
This guide shows how to install an Apache Hadoop test environment, which will enable to implement basic commands.
In this category dedicated to MOOC Big Data, you will attend various courses on data science,data lake, Apache Hadoop.
Big Data Analysis(PDF)Scale up Apache Hadoop clusters to handle the increasing volume, variety, and velocity of data.
Intel offers a choice of big data solutions based on industry-standard chips,servers, and the Apache Hadoop* framework.
The guide is designed to have a first contact with Apache Hadoop, not to stablish a pre-production or production environment.
Read how Apache Hadoop* provides a foundation you can implement today for targeted value, and then expand to meet growing needs.
Thanks to the knowledge of experienced teachers, your R,Python and Apache Hadoop programming courses will become a pleasure.
One emerging data storage tool that's similar to a data warehouse is a data lake,which was brought about by disruptive low-cost technologies such as Apache Hadoop.
Cloudera and Intel deliver enterprise-grade innovations to the Apache Hadoop* framework in security, performance, management, and governance.
The Apache Hadoop software collection is a framework that allows for the distributed processing of large data sets across clusters of computers.
Very large file systems,embodied by applications like Apache Hadoop and Google File System, use some database file system concepts.
With the Lenovo Big Data Validated Design for Cloudera Enterprise,Lenovo delivers a certified solution for both Apache Hadoop and Apache Spark environments.
As the de facto platform for big data, apache hadoop has evolved significantly over the last years, with more than 60 releases bringing new features.
Other than running on a single machine,it also supports the distributed processing frameworks Apache Hadoop, Apache Spark, and Apache Flink.
Using simple programming models, Apache Hadoop is a framework that allows for the distributed processing of large data sets across clusters of computers.
The objective of this work is to apply parallel anddistributed processing technologies in the context of calculation of environmental data using the apache hadoop and apache spark frameworks.
As the Apache Hadoop ecosystem grows while its core matures, there are now several companies providing business-class Hadoop distribution and services.
In October 2011, Oracle announced the"Big Data Appliance",which integrates R, Apache Hadoop, Oracle Linux, and a NoSQL database with the Exadata hardware.
Architectures like Apache Hadoop let companies store data at massive scale in full atomic format, providing data points for initial machine learning and training.
It can also be integrated into Data Flow frameworks like Apache Spark, Apache Hadoop, and Apache Flink using the abstracted Rabit and XGBoost4J.
However, apache hadoop has been designed for dedicated and homogeneous clusters, a limitation that creates challenges for those who wish to use the framework in other circumstances.
Hortonworks develops, distributes andsupports the only 100 per cent open source distribution of Apache Hadoop explicitly architected, built and tested for enterprise-grade deployments.
The apache hadoop data processing software is immersed in a complex environment composed of huge machine clusters, large data sets, and several processing jobs.
The system was written in various languages, including Java, C++, and Prolog, andruns on the SUSE Linux Enterprise Server 11 operating system using Apache Hadoop framework to provide distributed computing.
Our approach is based on distribution technologies provided by the apache hadoop framework to enable analytical sparql queries to be run in a distributed manner over rdf data stored in a distributed database.