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Cloudera's Gap Analysis Paper

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Hadoop has evolved greatly since early 2008, where the Apache Software Foundation initially recognized its importance and promoted it to a top-level open source project. The ‘big data deluge’ is compounding, with IT teams taking a “grab the data first, and figure out what to do with it later” approach. There is no way to predict big data applications in the future. The choice of infrastructure around an organization’s big data is an important one to make. With its constant evolution, IT teams are having a difficult time properly acquiring, deploying, and integrating Hadoop properly. Thus, while Hadoop is an open source project, many vendors began offering their own distributions of Hadoop, each with their own perks. This is an analysis of what …show more content…

Cloudera was founded in 2008 by a group of engineers from Yahoo, Google, and Facebook. The company is also currently the frontrunner in Hadoop environment, with 53% of Hadoop platform market share (excluding pay-as-you-go services such as Amazon EMR). Part of this success can be attribute to strong corporate partnerships, with companies such as Oracle and MongoDB pushing its services (Top 6 Hadoop Vendors providing Big Data Solutions in Open Data Platform). Cloudera offers their core system, CDH, in combination with subscriptions services such as Cloudera Enterprise and purports to be the most popular distribution of Apache Hadoop. CDH offers the core elements of Hadoop with all of the integration work completed for the organization with additional components, including Hue, Cloudera’s user interface. Cloudera Manager is also included with the Express and Enterprise subscriptions, allowing for automated deployment, configuration, and cluster …show more content…

With a highly specialized version of HBase, which claims higher reliability and security, MapR promotes itself as the only distribution that provides full data protection with no single point of failure (deRoos, 2014). The MapR distribution is also production-ready and can run online analytical processing and applications on a single platform, touting a lower “total cost of ownership” resulting from less administrative burdens (Why

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Hadoop [8] is an open source implementation of MapReduce programming model which runs in a distributed environment. Hadoop consists of two core components namely Hadoop Distributed File System (HDFS) and the MapReduce programming with the job management framework. HDFS and MapReduce both follow the master-slave architecture. A Hadoop program (client) submits a job to the MapReduce framework through the jobtracker which is running on the master node. The jobtracker assigns the tasks to the tasktrackers running on many slave nodes or on a cluster of machines.

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2.7 Observations from GMPCS Model Based on the above model, several observations can be made as follows. Observation_1: According to an interoperability feature between CSPs, a storage service will be hosted over a pool of resources that are in different geographical locations. Furthermore, different technologies, protocols, and security strategies are applied by each CSP within its datacentres to facilitate managing an environment to protect both resources and data. The technologies and strategies, therefore, might be disparate in terms of efficiency, and the type of storage network or storage system might be varied as well.

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By integrating Bandwidth’s solutions onto the Restcomm platform, developers and organizations can quickly deploy and provision communications solutions on any cloud based platform from; on-premises, hosted, or hybrid. Telestax’s customers want reliable, cost-effective, and integrated communications solutions. Customers want to incorporate collaboration functionality into deployed applications to increase their lifespan and usefulness. Bandwidth combined Telestax expands the Restcomm developer network to deliver powerful voice and real-time communications applications.

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Every page, folder, template, group and user record has an audit trail that captures every change made, with the date/time and user stamp to indicate who made the change and when. Users with appropriate security access can view this information, and reporting is also available to pinpoint specific types of changes or to narrow down records to specific time frames. 6. How does the system ensure availability, security and privacy of data on shared

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KELSER – ‘Technology forward’, provides a cloud solution for disaster recovery and targets especially small and medium business (SMB’s) which can provide a speedy recovery with lesser hardware dependency. The main advantage of using Kelser products is that it provides scalability for its DR services and can be purchased on ‘a need’ basis. The biggest drawback is that, it does not address all the business units of an organization and hence the DR process cannot be address at an organizational level which leads to lack of ownership in dealing with disaster recovery (KELSER, 2016).

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David Ward Dr. Powell Principles of Info Systems November 12, 2015 Cloud Project The purpose of this project is to investigate different cloud services that are being offered in today’s marketplace, analyze their different benefits as well as negatives, and pick a company who offers services that suits my business best. Regardless of what website we are looking at, all of the websites are offering the same thing, which is a cloud service. Cloud computing service providers can provide IT services, ranging from storage space all the way to complete applications. This has become a viable service, as these companies have the ability to provide IT services with higher quality and more efficiently than their customers can.

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Sqoop: A project for transferring/importing data between relational databases and Hadoop. Oozie: An orchestration and work flow management for dependent Hadoop jobs. Figure 2 gives an overview of the Big Data analysis tools which are used for efficient and precise data analysis and management jobs. The Big Data Analysis and management setup can be understood through the layered structured defined in the figure. The data storage part is dominated by the HDFS distributed file system architecture and other architectures available are Amazon Web Service, HBase and Cloud Store etc.

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Hadoop MapReduce is a framework for processing large data sets in parallel across Hadoop cluster. It uses two steps Map and Reduce process. An initiated job has a map and reduces phases. The map phase counts the words in each document, then reduce phase aggregates the per document data into word counts spanning the entire collection. The reduce phase use the results from map tasks as input to a set of parallel reduce tasks and it consolidate the data into final result.

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During the 17th, 18th and 19 centuries, many of the western arts, in Europe and America were inspired by the invasion of Japanese blueprint and ukiyo-e print (floating world), which exploded the world of the Arts. It produces paints and prints illustrating of the everyday activities, the significances of the culture, local natives, landscape, female beauties and others. One of the American artists that was drastically mesmerized by the Japanese art is Helen Hyde. Helen Hyde is an American female artist etcher, printmaking and engraver for many years.

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The quality of the baseline can be measured with logs, configured locally, or configured to send information to a central logging server. With that and related runtime information, we identify a baseline system state. In addition, IDS tools such as Tripwire to help protect the integrity of that baseline system. Tripwire is a complete security configuration management solution. It not only audit changes and ensure system integrity, but also assess all IT configurations against known benchmarks, security standards and best practices.

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Big Data There are many different definitions for Big Data. SAS (n.d.) an analytical software company describes it as, “a popular term used to describe the exponential growth and availability of data, both structured and unstructured.” Many think Big Data just came into existence but it has been around for years. Banks, retail, advertisers have been using big data for marketing purposes.

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