With the evolution and advancement of technology, the amount of data that is being generated is ever increasing. Sources of … Introduction to Big Data and Hadoop. We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. First the big data are characterized through the model of 3V to 5V extended.
The problematic of big data is distinguished from that of Business Intelligence. The economic and societal challenges associated with big data are discussed by presenting various examples of use in different areas of activity.
With that in mind, generally speaking, big data is: large datasets the category of computing strategies and technologies that are used to … This chapter provides an overview of big data storage technologies.
It is the result of a survey of the current state of the art in data storage technologies in order to create a cross-sectorial. With the developments of the cloud storage, big data has attracted more and more attention. Due to the emergence of the Internet, the big data technology will accelerate the innovation of the enterprises, lead the revolution analysis, Storage, Transport and processing the data using the existing traditional techniques.
This paper introduces Big Data Analysis and storage. Though, a wide variety of scalable database tools and techniques has evolved. Big data is a blanket term for the non-traditional strategies and technologies needed to gather, organize, process, and gather insights from large datasets.
While the problem of working with data that exceeds the computing power or storage of a single computer is not new, the pervasiveness, scale, and value of this type of computing has greatly expanded in recent years. Data is a collection of facts, such as values or measurements. Data can be numbers, words, observations or even just descrip-tions of things.
Storing and retrieving vast amounts of information, as About this tutorial Hadoop is an open-source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models.
It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. All Rights Reserved. Why Hadoop? Big Data analytics and the Apache Hadoop open source project are rapidly emerging as the preferred solution to address business and technology trends that are disrupting traditional data management … Fraud Detection: Big Data analysis combined with customer behavior and historical transaction data is helping Credit card companies to identify fraudulent activities.
Skip to content. By evan Jan 10, This course is for those new to data science and interested in understanding why the Big Data Era has An introduction to Big Data Universitetet i oslo Big Data Modeling and Management Systems Coursera Big Data is also geospatial data, 3D data, audio and video, and unstructured text, including log files and social media.
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Consider the timeline: MapReduce implementation by Google came from work that dates back to , published in MR is based on the economics of data centers from a decade ago. Since that time, so much has changed: multi-core processors, large memory spaces, 10G networks, SSDs, and such, have become cost-effective in the years since. These dramatically alter the trade-offs for building fault-tolerant distributed systems at scale on commodity hardware.
Moreover, even our notions of what can be accomplished with data at scale have also changed. No, not particularly. It is unlikely that senior executives in publishing would have bothered to read such an outlandish engineering proposal. The marketing of this book itself will be based on a large-scale, open source, graph query engine described in subsequent chapters.
The shape of the underlying systems has changed so much since MR at scale on commodity hardware was first formulated. Furthermore, the applications of math for data at scale are quite different than what would have been conceived a decade ago. Popular programming languages have evolved along with that to support better software engineering practices for parallel processing.
Agneeswaran considers these topics and more in a careful, methodical approach, presenting a thorough view of the contemporary Big Data environment and beyond. The chapters include historical context, which is crucial for key understandings, and they provide clear business use cases that are crucial for applying this technology to what matters. The arguments provide analyses, per use case, to indicate why Hadoop does not particularly fit—thoroughly researched with citations, for an excellent survey of available open source technologies, along with a review of the published literature for that which is not open source.
This book explores the best practices and available technologies for data access patterns that are required in business today beyond Hadoop: iterative, streaming, graphs, and more. Real-time analytics are the only conceivable solutions in those cases. Agneeswaran guides the reader through the architectures and computational models for each, exploring common design patterns. He includes both the scope of business implications as well as the details of specific implementations and code examples.
Along with these frameworks, this book also presents a compelling case for the open standard PMML, allowing predictive models to be migrated consistently between different platforms and environments. FOREWORD xi This is precisely the focus that is needed in industry today—given that Hadoop was based on IT economics from , while the newer frameworks address contemporary industry use cases much more closely.
Moreover, this book provides both an expert guide and a warm welcome into a world of possibilities enabled by Big Data analytics. Vineet has been instrumental and enabled me to take up book writing. He has been kind enough to give me three hours of official time over six to seven months—this has been crucial in helping me write the book. Any such scholarly activity needs consistent, dedicated time—it would have been doubly hard if I had to write the book in addition to my day job.
Vineet just made it so that at least a portion of book writing is part of my job! Writing a book while work- ing in the IT industry can be an arduous job.
Thanks to Pankaj for enabling this and similar activities. Thanks, Praveen, for the support! I also wish to thank Dr. He has been a person I look up to and an inspiration to excel in life. Nitin, being a former professor at the Indian Institute of Management IIM Indore, exemplifies my high opinion of academicians in general!
He has been my go-to man. Special thanks to Pranay. She has a very good understanding of Storm—in fact, she is considered the Storm expert in the organization. She has also devel- oped an inclination to understand machine learning and Spark. It has been a pleasure having her on the team. Thanks, Jayati! Sai Sagar, Software Engineer at Impetus, has also been instru- mental in implementing machine learning algorithms over GraphLab.
Thanks, Sagar, nice to have you on the team! Thanks, Ankit, for that and some of our nice discussions on machine learning! I would also like to thank editor Jeanne Levine, Lori Lyons and other staff of Pearson, who have been helpful in getting the book into its final shape from the crude form I gave them! Thanks also to Pear- son, the publishing house who has brought out this book. I would like to thank Gurvinder Arora, our technical writer, for having reviewed the various chapters of the book.
I would like to take this opportunity to thank my doctoral guide Professor D. I owe a lot to him—he has shaped my technical thinking, moral values, and been a source of inspiration throughout my profes- sional life. Not only Prof. I also wish to express my gratitude to Joydeb Mukherjee, formerly senior data scientist with Impetus and currently Senior Technical Specialist at MacAfee.
Joydeb reviewed the Introduction chapter of the book and has also been a source of sound-boarding for my ideas when we were working together. This helped establish my beyond- Hadoop ideas firmly. He has also pointed out some of the good work in this field, including the work by Langford et al. I would like to thank Dr.
He has also been kind enough to review the book. He was also the organizer of the Strata conference in California in February when I gave a talk about some of the beyond-Hadoop concepts. That talk essentially set the stage for this book. I also take this opportunity to thank the Strata organizers for accepting some of my talk proposals.
Paco Nathan for reviewing the book and writing up a foreword for it. His comments have been very inspiring, as has his career! He is one of the folks I look up to. Thanks, Paco! My other team members have also been empathetic—Pranav Ganguly, the Senior Architect at Impetus, has taken quite a bit of load off me and taken care of the big data governance thread smoothly.
It is a pleasure to have him and Nishant Garg on the team. I wish to thank all my team members. Without a strong family backing, it would have been difficult, if not impossible, to write the book. My wife Vidya played a major role in ensuring the home is peaceful and happy. She has sacrificed signifi- cant time that we could have otherwise spent together to enable me to focus on writing the book.
My kids Prahaladh and Purvajaa have been mature enough to let me do this work, too. I also wish to thank my parents for their upbringing and inculcating morality early in my life. Finally, as is essential, I thank God for giving me everything. I am ever grateful to the almighty for taking care of me. He has filed patents with U. He has published in leading journals and conferences, including IEEE transactions. His recent publi- cations have appeared in the Big Data journal of Liebertpub.
He lives in Bangalore with his wife, son, and daughter, and enjoys research- ing ancient Indian, Egyptian, Babylonian, and Greek culture and philosophy.
Perhaps you are a video service provider and would like to opti- mize the end user experience by choosing the appropriate content distribution network based on dynamic network conditions. Or you are a government regulatory body that needs to classify Internet pages into porn or non-porn in order to filter porn pages—which has to be achieved at high throughput and in real-time. How you wish you had known that the last customer who was on the phone with your call center had tweeted with nega- tive sentiments about you a day before.
Or you are a healthcare insurance provider for whom it is imperative to compute the probabil- ity that a customer is likely to be hospitalized in the next year so that you can fix appropriate premiums. Or you work for an electronic manufacturing company and you would like to predict failures and identify root causes during test runs so that the subsequent real-runs are effective.
Welcome to the world of possibilities, thanks to big data analytics. The only difference between the terms analysis and analytics is that analytics is about analyzing data and convert- ing it into actionable insights. The term Business Intelligence BI is also used often to refer to analysis in a business environment, possibly originating in a article by Peter Luhn Luhn Lots of BI applications were run over data warehouses, even quite recently.
The term big data seems to have been used first by John R. Mashey, then chief scientist of Silicon Graphics Inc. The term was also used in a paper Bryson et al. The report Laney from the META group now Gartner was the first to iden- tify the 3 Vs volume, variety, and velocity perspective of big data. Though the MR paradigm was known in the functional pro- gramming literature, the paper provided scalable implementations of the paradigm on a cluster of nodes.
The paper, along with Apache Hadoop, the open source implementation of the MR paradigm, enabled end users to process large data sets on a cluster of nodes—a usability paradigm shift. For example, imagine that you have a set of stocks and the set of values of those stocks at vari- ous time points. It is required to compute correlations across stocks—can you check when Apple falls? What is the prob- ability of Samsung too falling the next day?
The computation cannot be split into independent chunks—you may have to compute correlation between stocks in different chunks, if the chunks carry different stocks. If the data is split along the time line, you would still need to compute correlation between stock prices at different points of time, which may be in different chunks.
One is the overhead of fetching data from HDFS for each iteration which can be amortized by a distributed caching layer , and the other is the lack of long-lived MR jobs in Hadoop. Typically, there is a termination condition check that must be executed outside of the MR job, so as to deter- mine whether the computation is complete.
The other perspective of Hadoop suitability can be understood by looking at the characterization of the computation paradigms required for analytics on massive data sets, from the National Academies Press NRC Basic statistics: This category involves basic statistical opera- tions such as computing the mean, median, and variance, as well as things like order statistics and counting.
The operations are typically O N for N points and are typically embarrassingly parallel, so perfect for Hadoop. Linear algebraic computations: These computations involve linear systems, eigenvalue problems, inverses from problems such as linear regression, and Principal Component Analysis PCA. Moreover, a formulation of multivariate statistics in matrix form is difficult to realize over Hadoop. Examples of this type include kernel PCA and kernel regression.
Generalized N-body problems: These are problems that involve distances, kernels, or other kinds of similarity between points or sets of points tuples.
Computational complexity is typically O N2 or even O N3. The typical problems include range searches, nearest neighbor search problems, and non- linear dimension reduction methods.
Graph theoretic computations: Problems that involve graph as the data or that can be modeled graphically fall into this cat- egory. The computations on graph data include centrality, com- mute distances, and ranking. When the statistical model is a graph, graph search is important, as are computing probabilities which are operations known as inference. Some graph theoretic computations that can be posed as linear algebra problems can be solved over Hadoop, within the limitations specified under giant 2.
Euclidean graph problems are hard to realize over Hadoop as they become generalized N-body problems. More- over, major computational challenges arise when you are deal- ing with large sparse graphs; partitioning them across a cluster is hard.
Optimizations: Optimization problems involve minimiz- ing convex or maximizing concave a function that can be referred to as an objective, a loss, a cost, or an energy func- tion.
These problems can be solved in various ways. Stochas- tic approaches are amenable to be implemented in Hadoop. Mahout has an implementation of stochastic gradient descent. Linear or quadratic programming approaches are harder to realize over Hadoop, because they involve complex iterations and operations on large matrices, especially at high dimensions.
One approach to solve optimization problems has been shown to be solvable on Hadoop, but by realizing a construct known as All-Reduce Agarwal et al. However, this approach might not be fault-tolerant and might not be generalizable. Conjugate gradient descent CGD , due to its iterative nature, is also hard to realize over Hadoop.
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