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Introduction to Hadoop Technology


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It is a small presentation on introducton and working of hadoop

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Introduction to Hadoop Technology

  1. 1. HADOOP TECHNOLOGY Presented by :- Manish.S. Borkar Poly 6th sem, IT branch, Nagpur Polytechnic, Nagpur
  2. 2. Cluster of machines running Hadoop at Yahoo!
  3. 3. Processing Vcards: Example of VCARD •BEGIN : VCARD •N: Manish Borkar •INSTT : Nagpur Polytechnic, Nagpur •DESIG : Student •EMAIL : •URL : •URL : •END:VCARD
  4. 4. • 1 GB – 10 GB – 100 GB --- limits • More Investments • -- 10 TB – 100 TB --- again limits • Data from Facebook, Twitter, RFID readers, sensors. • Structured / Unstructured
  5. 5. Here is come the solution Hadoop….
  6. 6. •Hadoop Distributed File System (HDFS) – a distributed file- system that stores data on commodity machines, providing very high aggregate bandwidth across the cluster. •Hadoop YARN – a resource-management platform responsible for managing compute resources in clusters and using them for scheduling of users' applications. • Hadoop MapReduce – a programming model for large scale data processing.
  7. 7.  Name node:- The HDFS namespace is a hierarchy of files and directories. Files and directories are represented on the NameNode by inodes.  Data Node:- Each block replica on a DataNode is represented by two files in the local native filesystem. The first file contains the data itself and the second file records the block's metadata  HDFS Client:- User applications access the filesystem using the HDFS client, a library that exports the HDFS filesystem interface.
  8. 8. • MapReduce is an associated implementation for processing and generating large data sets. • A Map-Reduce job usually splits the input data-set into independent chunks which are processed by the map tasks in a completely parallel manner. • A MapReduce job is a unit of work that the client wants to be performed: it consists of the input data, the MapReduce program, and configuration information. Hadoop runs the job by dividing it into tasks, of which there are two types: map tasks and reduce tasks • Mapreduce is a progrmming model for processing and generating large data sets with a parallel, distributed algorithms on a cluster
  9. 9. THE PROGRAMMING MODEL OF MAPREDUCE  Map, written by the user, takes an input pair and produces a set of intermediate key/value pairs. The MapReduce library groups together all intermediate values associated with the same intermediate key I and passes them to the Reduce function.
  10. 10.  The Reduce function, also written by the user, accepts an intermediate key I and a set of values for that key. It merges together these values to form a possibly smaller set of values
  12. 12.  Pig  Mahout  Hive  Avro  Strom
  13. 13.  Amazon web  Services  Apache Bigtop  Cascading  Cloudera  Cloudspace  Datameter
  14. 14. • As the amount of data being stored around the globe continues to rise and the cost of technologies that enable the extraction of meaningful patterns . As the amount of data and cost of handling it increases this make difficult to organization to affort the cost and store the high amount of data and to process it.Then the hadoop is the best choice for the growing world by its easy handling and large storing of data.
  15. 15. [1] UNIX Filesystems: Evolution, Design, and Implementation. Wiley Publishing, Inc., 2003. [2] The diverse and exploding digital universe. universe, 2009. [3] Hadoop., 2009. [4] [5] HDFS (hadoop distributed file system) architecture. http://hadoop. design.html, 2009.