Data Life Cycle
The data life cycle is pictorial
defined as show below:
As we see, in our current system,
we capture/ Extract our data, then we store it and later we process for
reporting and analytics.
But in case of big data, the
problem lies in storing and then processing it faster. Hence Hadoop takes this
portion, where it stores the data in effective format (Hadoop distributed File
System) and also process using its engine (Map Reduce Engine).
Since Map Reduce engine or Hadoop engine
need data on HDFS format to process, We have favorable tools available in
market to do this operation.
As an example, Scoop is a tool
which converts RDBMS to HDFS. Likewise we have SAP BOD to convert sap system
data to HDFS.
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