Recent development in Internet-scale data applications and services, combined with the proliferation of cloud computing, has created a new computing model for data intensive computing best characterized by the MapReduce paradigm. The MapReduce computing paradigm, pioneered by Google in its Internet search application, is an architectural and programming model for efficiently processing massive amount of raw unstructured data. With the availability of the open source Hadoop tools, applications built based on the MapReduce computing model are rapidly growing.
Hadoop is open source software framework built in java program for distributed storage and processing data on computer cluster environment.
Massive amount of data could be effectively stored under Hadoop platform. Problems faced in big data projects can be solved using hadoop concepts.
Hadoop projects is done by our concern in cloud domain for computer science and information technology final year students and research scholars. Hadoop Projects core concept can be divided into two blocks. Hadoop distributed file system: Large amount of data could be accessed under Hadoop Projects.
It is a software framework and a programming model. Vast data in parallel and commodity hardware could be simplified by MapReduce framework. Fault tolerant data could be processed by the commodity hardware in mapreduce. Thousand of nodes in a network could be accessed by peta bytes of data. In structured and unstructured data computation processes occur.
Structured data are referred as database so Hadoop are known as file system. Two fundamental process are carried under map reduce framework they are Mapping: Master node takes out large problem input and divides it into smaller sub problems and gives away to worker nodes. Smaller problem are solved by worker and handed to master node in Hadoop Projects.
To sub problem the answers are taken by the master node and links them in a predefined way to derive answer to the original problem.
Intermediate values are taken and low down to smaller solution. Hadoop cluster architecture Various nodes are present in hadoop cluster architecture.
Hadoop cluster can classified in to master node and slave node. Slave nodes in Hadoop cluster are contained for data processing in server side in Hadoop Projects.
Types of Master nodes.
HDFS layer use name node to allocate names. Individual task to individual node. HDFS layer use data node to allocate data.
For each machine job scheduled could be identified in a cluster Types of slave node. Map reduce layer is carried under task tracker node in Hadoop Projects. Applications of Hadoop Projects.this thesis, we explore the possibility of using the Master Worker abstraction as the basis for building the MapReduce framework.
This framework targets applications with lower. The goal of this thesis is to bring Hadoop MapReduce framework to a mobile cloud environment such that it solves these bot- tlenecks involved in big data processing. In this thesis, we study the tradeoff between communication cost and parallelism, for one-pass MapReduce algorithms for matrix multiplication.
We measure parallelism either. EVOLVING INSIDER THREAT DETECTION USING STREAM ANALYTICS AND BIG DATA by PALLABI PARVEEN, BS, MS DISSERTATION Presented to the Faculty of The University of Texas at Dallas.
AN EXPERIMENTAL STUDY OF MONOLITHIC SCHEDULER ARCHITECTURE IN CLOUD COMPUTING SYSTEMS BY THESIS Submitted in partial ful llment of the requirements for the degree of Master of Science in Computer Science in the Graduate College of the University of Illinois at Urbana-Champaign, data analytics frameworks like MapReduce , graph.
Lean MapReduce: A B-tree Inspired MapReduce Framework Dynamic provisioning of compute resources to adhoc MapReduce clusters based on workload size Arinze George Akubue Master’s Thesis Spring Lean MapReduce: A B-tree Inspired MapReduce Framework Arinze George Akubue June 6,