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BIG DATA

Big data means really a big data, it is a collection of large datasets that cannot be processed using traditional computing techniques.

BIG DATA

Hadoop is an open-source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.

Big data is not merely a data, rather it has become a complete subject, which involves various tools, technqiues and frameworks.

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HADOOP HDFS

Hadoop File System was developed using distributed file system design. It is run on commodity hardware. Unlike other distributed systems, HDFS is highly faulttolerant and designed using low-cost hardware.

HDFS holds very large amount of data and provides easier access. To store such huge data, the files are stored across multiple machines. These files are stored in redundant fashion to rescue the system from possible data losses in case of failure. HDFS also makes applications available to parallel processing.

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HADOOP MAP-REDUCE

MapReduce is a processing technique and a program model for distributed computing based on java. The MapReduce algorithm contains two important tasks, namely Map and Reduce.

The major advantage of MapReduce is that it is easy to scale data processing over multiple computing nodes. Under the MapReduce model, the data processing primitives are called mappers and reducers.

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OUR CLIENTS

We provide our clients with Web-based services and solutions in ERP, Business Intelligence, Data Management, Cloud Computing and Quality Assurance services.We execute the statement of work, assume risks, and ensure that the work is done on time and on budget.

TECHNICAL PARTNER

We provide our clients with Web-based services and solutions in ERP, Business Intelligence, Data Management, Cloud Computing and Quality Assurance services.We execute the statement of work, assume risks, and ensure that the work is done on time and on budget.

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