Hadoop Essentials:-
Intro:-
Intro:-
Hadoop is a system for parallel processing of massive data distributed across many nodes(computers). Many nodes join together to form a Hadoop cluster. A cluster can even have nodes hosted in a different geographical region. Normal practice is to keep the nodes in a cluster closer.Use of Hadoop enables in speeding up large computations and hiding I/O latency through increased concurrency.
Hadoop is designed to work on large data processing tasks like searching user generated logs, indexing of websites and any such massive data. It shines in processing large volume of data sets running into terabytes or petabytes that the traditional systems cannot effectively store or process in any reasonable amount of time frame. Hadoop splilts the large data into chunks and stores each chunk on different nodes in the cluster. It stores data chunks locally on the computational node(s) rather than moving data from a central point to the individual nodes for computation. This makes the computation faster as the data to process is already available on the local node. This distributed file system (HDFS= Hadoop File System ) is the back bone of Hadoop and its effectiveness.
Hadoop uses the map reduce programming practice to split a large computational problem into small units, work on different units in parallel and finally collate the individual results to calculate the final result. Hadoop is written in Java. But Hadoop programs can be written in many languages.
below is the good link from one of the blogger vishal jogiya https://drive.google.com/open?id=0B6DVaY9UQtVjfjVacnVUVXRPRXNfVGZWU2hLUjJxbnhzODBzR05DQ2R0dUw0b3JRaVlOeVE
Hadoop is designed to work on large data processing tasks like searching user generated logs, indexing of websites and any such massive data. It shines in processing large volume of data sets running into terabytes or petabytes that the traditional systems cannot effectively store or process in any reasonable amount of time frame. Hadoop splilts the large data into chunks and stores each chunk on different nodes in the cluster. It stores data chunks locally on the computational node(s) rather than moving data from a central point to the individual nodes for computation. This makes the computation faster as the data to process is already available on the local node. This distributed file system (HDFS= Hadoop File System ) is the back bone of Hadoop and its effectiveness.
Hadoop uses the map reduce programming practice to split a large computational problem into small units, work on different units in parallel and finally collate the individual results to calculate the final result. Hadoop is written in Java. But Hadoop programs can be written in many languages.
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