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IEEE 2018-2019 BIG DATA/HADOOP BASED PROJECTS
we present real time IEEE Big Data projects for computer science and information science engineering students with high-quality Explanation and guidance and implementation.
This section consists of projects related to Big Data 2018-2019 IEEE project list. Big Data analysis has been a very hot active during past few years and holds the potential as yet largely untapped to allow decision makers to track development progress.
Latest Big Data topics, Latest Big Data Concepts for Diploma, Latest Big Data Concepts for Engineering
IEEE 2018-2019 bigdata (hadoop) project list on java based for MTech /BE / BTech / MCA / M.sc students in bangalore.
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Hadoop is an open-source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
Hadoop framework includes following Modules:
Hadoop Distributed File System (HDFS™)
Hadoop MapReduce is a software framework for easily writing applications which process big amounts of data in-parallel on large clusters (thousands of nodes) of commodity hardware in a reliable, fault-tolerant manner.
The term MapReduce actually refers to the following two different tasks that Hadoop programs perform:
The Map Task: This is the first task, which takes input data and converts it into a set of data, where individual elements are broken down into tuples (key/value pairs).
The Reduce Task: This task takes the output from a map task as input and combines those data tuples into a smaller set of tuples. The reduce task is always performed after the map task.
Hadoop Distributed File System (HDFS)
Hadoop File System was developed using distributed file system design. It is run on commodity hardware. Unlike other distributed systems, HDFS is highly fault tolerant and designed using low-cost hardware.HDFS holds very large amount of data and provides easier access. To store such huge data, the files are stored across multiple machines. These files are stored in redundant fashion to rescue the system from possible data losses in case of failure. HDFS also makes applications available to parallel processing.
Advantages of Hadoop
Hadoop framework allows the user to quickly write and test distributed systems. It is efficient, and it automatic distributes the data and work across the machines and in turn, utilizes the underlying parallelism of the CPU cores.
Hadoop does not rely on hardware to provide fault-tolerance and high availability (FTHA), rather Hadoop library itself has been designed to detect and handle failures at the application layer.
Servers can be added or removed from the cluster dynamically and Hadoop continues to operate without interruption.
Another big advantage of Hadoop is that apart from being open source, it is compatible on all the platforms since it is Java based.
Features of Hadoop
It is suitable for the distributed storage and processing.
Hadoop provides a command interface to interact with HDFS.
The built-in servers of namenode and datanode help users to easily check the status of cluster.
Streaming access to file system data.
HDFS provides file permissions and authentication.
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