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Download Document Category: Whitepaper
Subject: Data Analytics
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Keywords: Big Data, Big Data Analytics, big data storage, unstructured data
Document Price: Free
Document Date: 2011-07
Document Number:
Author(s): John Webster
# of Pages: 6
Short Description:
Now that cloud computing bandwagon is out of gas, vendors have jumped on the next one to roll down the pike: Big Data. And as with previous hype cycles, Big Data is now a source of confusion for users as vendors put forth their own unique and often conflicting definitions. The most common source of
Full Summary:
Now that cloud computing bandwagon is out of gas, vendors have jumped on the next one to roll down the pike: Big Data. And as with previous hype cycles, Big Data is now a source of confusion for users as vendors put forth their own unique and often conflicting definitions.
The most common source of confusion results from the conflation of Big Data storage with Big Data analytics. The term “Big Data” originated from within the open source community where there was an effort to develop analytics processes that were faster and more scalable the traditional data warehousing, and could extract value from the vast amounts of unstructured data produced daily by web users.
Big Data storage is related in that it also aims to address the vast amounts of unstructured data fueling data growth at the enterprise level. But the technologies underpinning Big Data storage like scale-out NAS and object-based storage have existed for a number of years and are relatively well understood
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The Big Data Landscape
Now that cloud computing bandwagon is out of gas, vendors have jumped on the next one to roll down the pike: Big Data. And as with previous hype cycles, Big Data is now a source of confusion for users as vendors put forth their own unique and often conflicting definitions.
The most common source of confusion results from the conflation of Big Data storage with Big Data analytics. The term “Big Data” originated from within the open source community where there was an effort to develop analytics processes that were faster and more scalable the traditional data warehousing, and could extract value from the vast amounts of unstructured data produced daily by web users.
Big Data storage is related in that it also aims to address the vast amounts of unstructured data fueling data growth at the enterprise level. But the technologies underpinning Big Data storage like scale-out NAS and object-based storage have existed for a number of years and are relatively well understood