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);
while(tokenizer.hasMoreToken());
word.set(tokenizer.nextToken());
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()) {
sum+=values.next().get();
}
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
