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Difference between nfs and hdfs ?

  • NFS (Network File System) is one of the oldest and popular distributed file storage systems. Whereas HDFS (Hadoop Distributed File System) is the recently used and popular one to handle big data.
Difference between nfs and hdfs
CRITERIA NFS HDFS
Data Size Support NFS can store and process small
amount of data.		 HDFS is mainly use to store and process big data.
Data Storage Data is stored on a single dedicated
hardware.		 The data blocks are distributed on the local drives of hardware.
Reliability No reliability, data is not available
in the case of machine failure.		 Data is stored reliably, data is available even after machine failure.
Data Redundancy NFS runs on single machine,
no chances of data redundancy.		 HDFS runs on a cluster of different machines, data redundancy may occur due to replication protocol.
Target Users Workgroup. Larger than AFS.
Domain Single Domain. Multi Domain.
Client Server Trust Client identity is trusted by default. Client identity is what os tells. No Kerberos Auth.
Compatability with O/S Same System calls as of O/S. Different Calls.Mainly used for non interactive programs.
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What is Apache Spark

  • Apache Spark is an open-source distributed cluster-computing framework.
  • Spark is a data processing engine developed to provide faster and ease-of-use analytics than Hadoop MapReduce.
  • Before Apache Software Foundation took possession of Spark, it was under the control of University of California, Berkeley’s AMP Lab.

What is Apache Hadoop

  • Apache Hadoop is an open-source framework written in Java that allows us to store and process Big Data in a distributed environment, across various clusters of computers using simple programming constructs.
  • To do this, Hadoop uses an algorithm called MapReduce, which divides the task into small parts and assigns them to a set of computers.
  • Hadoop also has its own file system, Hadoop Distributed File System (HDFS), which is based on the Google File System (GFS).
  • HDFS is designed to run on low-cost hardware.
CRITERIA SPARK HADOOP MAPREDUCE
Memory Let’s save data on memory with
the use of RDD’s.		 Does not leverage the memory of the hadoop cluster to maximum.
Disk usage Spark caches data in-memory
and ensures low latency.		 MapReduce is disk oriented.
Processing Supports real-time processing through
spark streaming.		 Only batch processing is supported
Installation Is not bound to Hadoop. Is bound to hadoop.
Storage Leverage exciting HDFS
Speed 10 – 100X faster. Fast.
Rsource management standalone YARN
Hadoop Vs Spark
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