BIG DATA ANALYSIS - created from Mind Map

DEFINITION

EXAMPLE

ENTERPRISE BIG DATA

TECHNOLOGIES NO SQL HADOOP MAP REDUCE DEFINITION USERS HDFS

Big data analytics is the process of examining large amounts of data of a variety of types (big data) to uncover hidden patterns, unknown correlations and other useful information. 

An example of big data might be petabytes (1,024 terabytes) or exabytes(1,024 petabytes) of data consisting of billions to trillions of records of millions of people -- all from different sources (e.g. Web, sales, customer contact center, social media, mobile data and so on).

When dealing with larger datasets, organizations face difficulties in being able to create, manipulate, and manage big data. Big data is particularly a problem in business analytics  because standard tools and procedures are not designed to search and analyze massive datasets.Big data may also be called enterprise big data. 

Hadoop is a free, Java-based programming framework that supports the processing of large data sets in a distributed computing environment. It is  the Apache project .

The Hadoop framework is used by majorGoogle, Yahoo and IBM, largely for applications involving search engines and advertising. The preferred operating systems are Windows and Linux

The Hadoop distributed file system (HDFS) is a distributed, scalable, and portable file-system written in Java for the Hadoop framework.HDFS stores large files (typically in the range of gigabytes to terabytes[13]) across multiple machines. It achieves reliability by replicating the data across multiple hosts, and hence does not require RAIDstorage on hosts. With the default replication value, 3, data is stored on three nodes: two on the same rack, and one on a different rack. Data nodes can talk to each other to rebalance data, to move copies around, and to keep the replication of data high. HDFS is not fully POSIX-compliant, because the requirements for a POSIX file-system differ from the target goals for a Hadoop application.

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BIG DATA ANALYSIS

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