Hadoop

This paper will outline and describe three vendors that provide the Hadoop NoSQL database program to enterprises. Each of these companies see themselves as uniquely different, thus positioning themselves within a market place that has begun to become highly competitive in the “Big Data” age. I will provide an outline of the talking points that will be discussed for each company, starting with a brief description of the Hadoop NoSQL open-source database program, then I will discuss each company on

Hadoop is an Apache open source software (java framework). It runs on cluster of commodity machines and provides both distributed storage and distributed processing of huge data sets. It is capable of processing data sizes ranging from Gigabytes to Petabytes. Architecture : Similar to master / slave architecture. The master is the Namenode and the Slaves are the data nodes. The Namenode maintains and manages blocks of datanodes. They are responsible for dealing with clients requests of data

6. What is spark? Spark is an in memory cluster computing framework which falls under the open source Hadoop project but does not follow the two stage map-reduce paradigm which is done in Hadoop distributed file system (HDFS) and which is meant and designed to be faster. Spark, instead support a wide range of computations and applications which includes interactive queries, stream processing, batch processing, iterative algorithms and streaming by extending the idea of MapReduce model. The execution

is considered as a powerful complement to Hadoop; it is more accessible, powerful and capable big data tool for tackling various big data problems. Its architecture is based on basically two kind of abstractions: Resilient Distributed Datasets (RDD) and Directed Acyclic Graphs (DAG). RDDs are a collection of data items that can be split and can be stored in-memory on worker nodes of a spark cluster. The DAG abstraction of Spark helps eliminate the Hadoop MapReduce multistage execution model. As

System Hadoop distributed file system is written in Java for Hadoop framework, it is scalable and portable FS. HDFS provide shell commands and Java application programming interface (API). [12] Data in a Hadoop cluster is broken down into smaller pieces (called blocks) and distributed throughout the cluster. In this way, the map and reduce functions can be executed on smaller subsets of larger data sets, and this provides the scalability that is needed for big data processing. [12] A Hadoop cluster

SUMMARY This paper explains the need of a sophisticated Cyber Defense system in organizations and Government agencies and how this can be achieved by using Cyber Analytics. INTRODUCTION Today’s “Cyber Domain” is growing rapidly to keep pace in an ever more competitive world. Businesses are adopting new ways of doing Business due to the increasing dependency on networked communication devices, network access points and cloud-based services. Building a sophisticated Computer Network Defense (CND)

1. INTRODUCTION “MapReduce Programming model is an associated implementation for processing and generating large datasets.” Prior to the development of MapReduce, Google was facing issues for processing large amounts of raw data, such as crawled documents, PageRank, Web request logs, etc. Even though computations were conceptually straightforward, the input data was very large. Also computations had to be distributed across many machines to reduce the computation time. So there was a need to find

MapReduce Parallel programming model if we ever get a chance. In Hadoop, there are two nodes in the cluster when using the algorithm, Master node and Slave node. Master node runs Namenode, Datanode, Jobtracker and Task tracker processes. Slave node runs the Datanode and Task tracker processes. Namenode manages partitioning of input dataset into blocks and on which node it has to store. Lastly, there are two core components of Hadoop: HDFS layer and MapReduce layer. The MapReduce layer read from and

opportunities for leveraging data for different purposes, resources are impacted, resulting in poor loading and response times. Across Industry verticals there is increasing adoption of Hadoop for Information Management and Analytics. Many have realized that in addition to new business related capabilities Hadoop also offers a host of options for IT Simplification and cost reduction. Initiatives such as Offloads are at the heart of such optimization. That said, capacity planning is the first step

Spatial data mining is a rising exploration field devoted to the advancement and utilization of novel computational procedures for the examination of big spatial datasets. It envelops methods for finding valuable spatial associations and patterns that are not stored in spatial datasets. Generally these procedures need to manage complex features with spatial data properties. The properties and relationships that have been contained in spatial data are different from transactional data. For instance