Introduction to Apache Hadoop Nowadays, people are living in the data world. It’s not easy to measure the total volume of data stored electronically, but an IDC estimate put the size of the “digital universe” at 0.18 zettabytes in 2006, and is forecasting a tenfold growth by 2011 to 1.8 zettabytes. A zettabyte is 〖10〗^21 bytes, or equivalently one thousand exabytes, one million petabytes, or one billion terabytes. That’s roughly the same order of magnitude as one disk drive for every person in the
Table of Contents List of Figures 3 Literature Review 4 History of Hadoop Technology 4 Applications of Hadoop 6 Main Components of Hadoop 6 MapReduce. 6 Map Step. 7 Reduce Step. 8 Hadoop Distributed File System (HDFS) 8 Advantages and Disadvantages of using Hadoop 11 Advantages. 11 Disadvantages. 11 Competitors to the Hadoop Technology 12 Conclusion 13 References 15 List of Figures Figure 1: MapReduce Programming Model 7 Figure 2: HDFS architecture 9 Figure 3: HDFS Operation
A Hadoop cluster consists a single master node and multiple worker nodes. The master node includes a JobTracker, TaskTracker, NameNode and DataNode. A slave or node acts as both DataNode and TaskTracker, though it could have data-only slave nodes and compute-only slave nodes.Hadoop requires JRE 1.6 (Java Runtime Environment)or higher. The standard shutdown scripts and start-up require Secure Shell to set up among nodes in the cluster. In a larger cluster,an extra NameNode called secondary
Testing with Big Data White paper for NTT DATA Gold Club 1.0 10-Mar-2014 REVISION HISTORY Version Effective Date (DD/MM/YYYY) Brief Description of Change Affected Section(s) Prepared By Reviewed By Approved By 1.0 10/03/2014 Varun Rathnakar Varun Rathnakar TABLE OF CONTENTS 1 Introduction 4 2 Characteristics of Big Data 5 3 Big Data Implementation 7 4 Big Data Testing Focus Areas 8 5 Conclusion 12 6 References 13 1 Introduction Big data refers to large datasets that are challenging to store
Variety. Variety is the different data types, representation and semantic interpretation. Dumbill (2012: 7) declares that “rarely does data present itself in a form perfectly ordered and ready for processing - it could be text from social networks, image data, a raw feed directly from a sensor source”. Value. Value is what matters to a person i.e. how valuable big data is to one. Benefits of big data Communication with customers Customers are not easy to study and predict; they look around a
lot of advantages of adopting a Hadoop-based approach, there are disadvantages too. In this section, I have highlighted some of the limitations that are related to the use of Hadoop. Below is a comprehensive list: 1. Security Concerns: Data security is the primary concern of a financial institution like a bank. It needs to protect its customer information, their transactional data and their unstructured data in the form of emails and social media information. Hadoop system is highly transparent to
reasoning over much larger data sets. Pivotal has its own Hadoop distribution, including a massively parallel processing (MPP) Hadoop SQL engine called HAWQ, which offers MPP-like SQL performance on Hadoop. 1) Pivotal Product Offerings and Functionality Pivotal HD – is Hadoop based Distribution for advanced analytics. Pivotal HD is an enterprise-ready; Hadoop distribution that delivers exponential business
Introduction: Big data is a hot topic in the Information Technology industry as it is a collection of data that describes the growth of the company, present in both structured and unstructured types. As the industry is dealing with large data, they are also concerned about the security of the data which is provided by big data security tools analytics. Big Data Security Analytics is a collection of security data sets which are large and complex and it becomes difficult to process using the traditional
environments, among which we highlight the project Eucalyptus (Liu et al. 2007), developed by the University of California. The following are some technology, such a model of programming, infrastructure and platforms for cloud computing. MapReduce/Hadoop The MapReduce (Dean and Ghemawat 2004) is a model of programming aimed at processing large volumes of data, where the user specifies your application through the sequence of MapReduce operations. The tasks of parallelism, fault tolerance, data
Google File Systems (GFS) is developed by Google to meet the rapidly growing demand of Google’s data processing needs. On the other hand, Hadoop Distributed File Systems (HDFS) developed by Yahoo and updated by Apache is an open source framework for the usage of different clients with different needs. Though Google File Systems (GFS) and Hadoop Distributed File Systems (GFS) are the distributed file systems developed by different vendors, they have been designed to meet the following goals: They
Health Informatics has been witnessing a tremendous modernization by leveraging the information technology and networking. Big Data tools offer a platform for organizing huge volume of data generated out of the medical informatics systems. They offer mechanism to store data in a distributed manner and offer parallel processing environment to process the large amount of data. Even though such platforms offer scalable way of managing large volume of data, those tools do not provide mechanism to get
Software application development at my company was initiated first out of security concerns. There were increasing numbers of security breaches reported in hospitals, banks, Yahoo, and other places that paused potential hazards (Snyder, 2014). We are in the financial Industry with huge volumes of sensitive data. Our Information Technology department expressed concerns that our SQL server was an easy target to those that may want to hack the system. Existing security measures and periodic training
personalization provided by RichRelevance to obtain consistency “across all channels, brands and devices”. (Podeszwa, 2015) Neiman Marcus is also using big data and Analytics through the use of Cloudera’s modern platform for data management, built on the Apache Hadoop open source tools for building big data applications. This platform is used to connect the multiple channels to collect massive amounts of data. This data is used to setup a more personalized experience for the consumer while shopping. For example
1. Preprocessing 2. Cleaning 3. Visualization According to paper [12] healthcare big data is analyzed by using the open source platform-Hadoop. Here we consider the three diseases. The Hadoop is the apache top level, open-source implementation of frameworks for reliable, scalable, distributed computing and data storage. Basically, Hadoop is the platform
Data Center: Data center, in the context of big data, is not only for data storage but it plays significant role to acquire, manage and organize the big data. Big data has uncompromising requirement for storage and processing capacity. Hence the data center development should be the focus for effective and rapid processing capacity. With the increasing scale of data centers, the operational cost should be reduced for the development of data centers. Today’s data centers are application-centric, powering
My main objective of pursuing a Master’s Degree in Computer Science is to expand my horizon and knowledge in this domain and also to place myself at a premier position in the highly competitive IT industry. I am primarily interested in Artificial Intelligence (AI) or Data Management. Having done an internship for 6 months, gave me an exposure to a professional environment in which I could implement my knowledge. It also gave me a deeper understanding of numerous fundamental concepts of Computer
Watson, a computer that can demonstrate its capabilities using natural language which can understand and answer questions as quickly as possible by quickly searching within its large scale data base and choosing out the vital words that right answer to the questions. Watson can do more than just answer questions in a game but rather be useful in any types of business and can also be used for scientific research and discoveries. With its growing platform, developers have been enhancing its capabilities
In the past number of years data has grown exponentially. This growth in data has created problems that and a race to better monitor, monetize, and organize it. Oracle is in the forefront of helping companies from different industries better handle this growing concern with data. Oracle provides analytical platforms and an architectural platform to provide solutions to companies. Furthermore, Oracle has provided software such as Oracle Business Intelligence Suite and Oracle Exalytics that have been
The new way of the world for more web software development is the set of applications of cloud based API. Governments and private sector organizations are trying to streamline operations as they expand their footprints. The new technologies are already cost efficiencies in infrastructure facilities like never before. Equally important, a number of new technological innovations now help us to re-imagine the efficiency and optimization of infrastructure and services in general. The biggest impact of
Log is a file that records the events which happens while an operating system or software runs [1]. It may include any activity such as information about a simple keystroke, the complete record of communication between two machines, system errors, inter-process communication, update events, server activities, client sessions, browsing history, etc. Logs provide a good insight into various states of a system at any instant and their analytical and statistical study can manage systems and mine useful