Examples of using Hadoop in English and their translations into Japanese
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It's an attractive proposition for companies that don't have the in-house technical expertise to deploy and manage Hadoop on their own.
The MapR Distribution including Hadoop provides real-time analysis and updates so that patients receive the individualized care they need when they need it.
Hadoop provides a high level of durability and availability while still being able to process computational analytical workloads in parallel.
What's more, these claims leave out the fact that Hadoop was originally not developed for BI or SQL execution.
Apache Hadoop is one such technology, and it is generally the software most commonly associated with Big Data.
As HDInsight leverages Apache Hadoop via the Hortonworks Data Platform, there is a high degree of fidelity with the Hadoop  ecosystem.
Access varied data from flat files, databases and big data environments such as Hadoop.
It does for real-time processing what Hadoop does for batch processing.
If a server running a task fails, Hadoop reruns that task on another machine until completion.
Hadoop is an open-source software framework that allows storing and processing of very large data sets in a distributed environment.
Once Hadoop  and the selected components is installed the next step is to configure Hadoop.
See how Sisense stacks up against enterprise BI, in-memory data discovery and visualization tools, open source BI platforms, and even Hadoop.
A Job JAR packages up all of the code and dependencies into a single JAR file for easy loading into Hadoop.
Businesses looking to modernize certainly need to plan for big data, and they ought to choose Hadoop right now, just on that basis.
Scalability and Performance- Unlike traditional systems that have a limitation on data storage, Hadoop is scalable as it operates in a distributed environment.
As to deploying Hadoop with Puppet, obviously the first thing you need is a problem that you need Hadoop to solve.
However, considering how big companies act in the turning point, Hadoop will never be thrown away.
Scalability- Unlike traditional systems that have a limitation on data storage, Hadoop is scalable because it operates in a distributed environment.
Comprehensive backup, recovery, and data management for both traditional and modern workloads- including NoSQL, Hadoop, software as a service(SaaS), and containers- all on one web-scale platform.
This Accenture study showed that Hadoop processing on the Google Cloud Platform had a better price-performance ratio than bare metal servers, and that performance tuning of cloud servers is a complex, but valuable exercise.