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Explaining Hadoop's HDFS (Hadoop Distributed File System)
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Big data is awash with acronyms at the moment, none more widely used than HDFS. Let's cut to the chase... it stands for Hadoop Distributed File System.
This is the system of distributing files that allows Hadoop to work on huge data sets at speed. It spreads blocks of data across different servers, as well as duplicating those blocks of data, and storing them distinctly.
Let's see why with an example.
Sarianne works in the financial markets, and runs a lot of predictive models to make sure her investments are minimum risk.
Utilising HDFS, her queries through Hadoop can run quickly because the data blocks are stored separately -- meaning all the computation can happen in one go, rather than queuing up behind each other.
As an added benefit, if one server fails (as one is bound to, given the amount of servers and disk drives needed to run big data projects) it won't stop Sarianne's models from pulling the data they need, because HDFS duplicated those blocks -- meaning Hadoop can return Sarianne's results in double quick time.
This is the system of distributing files that allows Hadoop to work on huge data sets at speed. It spreads blocks of data across different servers, as well as duplicating those blocks of data, and storing them distinctly.
Let's see why with an example.
Sarianne works in the financial markets, and runs a lot of predictive models to make sure her investments are minimum risk.
Utilising HDFS, her queries through Hadoop can run quickly because the data blocks are stored separately -- meaning all the computation can happen in one go, rather than queuing up behind each other.
As an added benefit, if one server fails (as one is bound to, given the amount of servers and disk drives needed to run big data projects) it won't stop Sarianne's models from pulling the data they need, because HDFS duplicated those blocks -- meaning Hadoop can return Sarianne's results in double quick time.
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