Hadoop

What is Big Data?
Big data is defined as the voluminous amount of structured, unstructured or semi-structured data that has huge potential for mining but is so large that it cannot be processed using traditional database systems. Big data is characterized by its high velocity, volume and variety that requires cost effective and innovative methods for information processing to draw meaningful business insights. More than the volume of the data – it is the nature of the data that defines whether it is considered as Big Data or not.
What is the default Storage of Hadoop framework?
HDFS (Hadoop Distributed File System)
What do the four V’s of Big Data denote?
IBM has a nice, simple explanation for the four critical features of big data:
a) Volume –Scale of data
b) Velocity –Analysis of streaming data
c) Variety – Different forms of data
d) Veracity –Uncertainty of data
Differentiate between Structured and Unstructured data
Data which can be stored in traditional database systems in the form of rows and columns, for example the online purchase transactions can be referred to as Structured Data. Data which can be stored only partially in traditional database systems, for example, data in XML records can be referred to as semi structured data. Unorganized and raw data that cannot be categorized as semi structured or structured data is referred to as unstructured data. Facebook updates, Tweets on Twitter, Reviews, web logs, etc. are all examples of unstructured data.
On what concept the Hadoop framework works?
1)HDFS – Hadoop Distributed File System is the java based file system for scalable and reliable storage of large datasets. Data in HDFS is stored in the form of blocks and it operates on the Master Slave Architecture.
2)Hadoop MapReduce-This is a java based programming paradigm of Hadoop framework that provides scalability across various Hadoop clusters. MapReduce distributes the workload into various tasks that can run in parallel. Hadoop jobs perform 2 separate tasks- job. The map job breaks down the data sets into key-value pairs or tuples. The reduce job then takes the output of the map job and combines the data tuples to into smaller set of tuples. The reduce job is always performed after the map job is executed.
What happens when a user submits a Hadoop job when the Job Tracker is down- does the job get in to hold or does it fail.
The Hadoop job fails when the Job Tracker is down.

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