MapReduce Projects

MapReduce Projects

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MapReduce Projects

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Functionalities of MapReduce:

  • Support replication [Distributed data store in to a number of machines]
  • Support concurrent data access [Local caching and fetch contents from remote servers]
  • Highly scalable for large data distributed and intensive applications [HDFS and Google file system]
  • It does not consider or process bad records

Motivations of using MapReduce:

  • Parallel across large clusters (hundreds/thousands of CPUs)
  • Large scale data processing
  • Reliable execution with ease of use
  • Automatic distribution and parallelization
  • Fault tolerance

Major MapReduce Tools and its usage:

MapReduce: Software framework that consists of single master node and slave nodes. The master node is taken full responsible for scheduling user jobs.

HDFS: It expansion is Hadoop Distributed File System which act as storage system to store large data sets (Terabytes amount of data) and also it streams data in high bandwidth.

Avro: Avro is a tool that most suitable to refer and represent the complex data structures within a MapReduce framework

Apache Mahout: It is implemented on top of the Apache Hadoop using MapReduce paradigm. It is a library of scalable machine learning algorithms

Oozie: Java Web Application framework to integrate multiple jobs on a single unit. It is used to schedule jobs.

Apache Spark: It is open source big data analytics framework building on top of the HDFS.

Disco: Open source and lightweight framework that used in distributed computing

Gigaom:  It is an open source software framework that compatible with C/C++ code

Spatial Hadoop: Extension of MapReduce framework that mainly created to access large scale spatial datasets

HIPI: Image processing library that designed to be used with the Apache Hadoop MapReduce

HBase: HBase is used for storing, and searching. It automatically shares the table across the multiple nodes.

SQOOP: It is a command line tool that controls the mapping between data storage and table.

GIS tools: Geographic Information Systems is used for geographical mapping which running on Hadoop.

MapReduce Integrated Tools and Usage:

  • Amazon Elastic MapReduce to Dash Mobile: Dash Mobile API allows users to integrate and access the Das functionality with other applications.
  • Misco: it is a distributed computing paradigm that mainly designed for mobile devices. It is compatible with Python and it is portable to run on any operating systems.

MapReduce Source Code Using Java:

/* Mapper Implementation

Public void map(Long writeable key, Text value, OutputCollector<Text, IntWriteable> output, Reporter reporter) throwsIOException {

      String line =value.toString();

      StringTokenizer tokenizer = new StringTokenizer(line);



      output.collect(word, one);


/* Reduce Implementation

Public void reduce(Text key, Iterator <IntWriteable> values,  OutputCollector<Text, IntWriteable> output, Reporter reporter) throwsIOException {

 int sum =0;

   while (values.hashNext()) {;


Output.collect(key, newIntWriteable(sum));


Current MapReduce Projects Titles:

  • MapReduce and R Based Flexible Computational Paradigm for Permutation Test of Complex Traits Massive Genetic Analysis
  • MapReduce Algorithm Based Weather Temperature Using Dig Data Prediction System
  • Big Data Processing Based Hierarchical Hadoop System in Geo Distributed Frameworks
  • Design Spark Based Intelligent K-Means for Big Data Clustering
  • Hadoop Based Multi Kernel Parallel SVM (Support Vector Machine) Optimization
  • Multi Source Heterogeneous Information Using FIM (Frequent Item-set Mining) Algorithm in Power Grid Intelligent Dispatching
  • SVD Technique to Extract and Retrieve Compressed Text Data Unique Patterns on Hadoop Apache MAHOUT System
  • User Preferences Set Similarity Based Big Data Applications Using Recommendation Framework
  • IP Spoofing Typed DDoS Attack Using Detection and Analysis Techniques Based on Hadoop
  • MapReduce for K-Nearest Neighbor Joins Parallel Computation
  • Spark and Hadoop Using Efficient Data Access Approaches on HPC Cluster With the Heterogeneous Storage
  • Earth Science Data Diagnosis and Visualization Over Spark and Hadoop
  • Blocking Non-Matched Documents Probabilistic Parallelization for Big Data
  • Design Approximation Algorithm Using Hadoop Architecture for Mapping Efficient DNA
  • Analytics and Strom with Hive for Real Time Streaming Data Processing and Storage