Modern Big Data Processing With Hadoop
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Modern Big Data Processing With Hadoop
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Author : V Naresh Kumar
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-03-30
Modern Big Data Processing With Hadoop written by V Naresh Kumar and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-30 with Computers categories.
A comprehensive guide to design, build and execute effective Big Data strategies using Hadoop Key Features -Get an in-depth view of the Apache Hadoop ecosystem and an overview of the architectural patterns pertaining to the popular Big Data platform -Conquer different data processing and analytics challenges using a multitude of tools such as Apache Spark, Elasticsearch, Tableau and more -A comprehensive, step-by-step guide that will teach you everything you need to know, to be an expert Hadoop Architect Book Description The complex structure of data these days requires sophisticated solutions for data transformation, to make the information more accessible to the users.This book empowers you to build such solutions with relative ease with the help of Apache Hadoop, along with a host of other Big Data tools. This book will give you a complete understanding of the data lifecycle management with Hadoop, followed by modeling of structured and unstructured data in Hadoop. It will also show you how to design real-time streaming pipelines by leveraging tools such as Apache Spark, and build efficient enterprise search solutions using Elasticsearch. You will learn to build enterprise-grade analytics solutions on Hadoop, and how to visualize your data using tools such as Apache Superset. This book also covers techniques for deploying your Big Data solutions on the cloud Apache Ambari, as well as expert techniques for managing and administering your Hadoop cluster. By the end of this book, you will have all the knowledge you need to build expert Big Data systems. What you will learn Build an efficient enterprise Big Data strategy centered around Apache Hadoop Gain a thorough understanding of using Hadoop with various Big Data frameworks such as Apache Spark, Elasticsearch and more Set up and deploy your Big Data environment on premises or on the cloud with Apache Ambari Design effective streaming data pipelines and build your own enterprise search solutions Utilize the historical data to build your analytics solutions and visualize them using popular tools such as Apache Superset Plan, set up and administer your Hadoop cluster efficiently Who this book is for This book is for Big Data professionals who want to fast-track their career in the Hadoop industry and become an expert Big Data architect. Project managers and mainframe professionals looking forward to build a career in Big Data Hadoop will also find this book to be useful. Some understanding of Hadoop is required to get the best out of this book.
Modern Technologies In Materials Mechanics And Intelligent Systems
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Author : X.Y. Huang
language : en
Publisher: Trans Tech Publications Ltd
Release Date : 2014-10-10
Modern Technologies In Materials Mechanics And Intelligent Systems written by X.Y. Huang and has been published by Trans Tech Publications Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-10-10 with Technology & Engineering categories.
Selected, peer reviewed papers from the 2014 4th International Conference on Intelligent System and Applied Material (GSAM 2014), August 23-24, 2014, Taiyuan, China
Big Data Machine And Deep Learning
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Author : Rajesh Kumar Mishra
language : en
Publisher: GRIN Verlag
Release Date : 2025-04-11
Big Data Machine And Deep Learning written by Rajesh Kumar Mishra and has been published by GRIN Verlag this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-11 with Computers categories.
Scientific Study from the year 2025 in the subject Computer Sciences - Artificial Intelligence, , language: English, abstract: In recent times, developments in artificial intelligence (AI) and machine learning (ML) have propelled improvements in systems and control engineering. We exist in a time of extensive data, where AI and ML can evaluate large volumes of information instantly to enhance efficiency and precision in decisions based on data. In control engineering, for instance, AI algorithms can anticipate system behaviors and autonomously modify controls to enhance performance for better efficiency and dependability. ML models, with their ability to learn, consistently enhance their predictions and choices as they handle additional data, enabling systems to dynamically adjust to evolving environments and operational circumstances. This swift adjustment enhances the functions of current systems and enables the creation of groundbreaking solutions, like self-driving cars and intelligent power grids, which were previously deemed unfeasible. The rapid expansion of digital data has propelled significant advancements in Big Data analytics, Machine Learning, and Deep Learning. These technologies are increasingly integrated across industries, facilitating automated decision-making, predictive modeling, and advanced pattern recognition. This chapter provides an in-depth review of recent progress in these domains, emphasizing breakthroughs in scalable data processing frameworks, cloud and edge computing, AutoML, explainable AI, transformer architectures, self-supervised learning, and generative models. Furthermore, it explores key applications in healthcare, finance, and autonomous systems, along with challenges such as data privacy, ethical concerns, and computational constraints. The discussion concludes with future directions, highlighting the potential of federated learning, neuromorphic computing, and novel algorithmic improvements to further expand AI's impact across disciplines.
Computer And Information Technology
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Author : Prasad Yarlagadda
language : en
Publisher: Trans Tech Publications Ltd
Release Date : 2014-02-06
Computer And Information Technology written by Prasad Yarlagadda and has been published by Trans Tech Publications Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-02-06 with Technology & Engineering categories.
Selected, peer reviewed papers from the International Forum on Computer and Information Technology (IFCIT 2013), December 24-25, 2013, Shenzhen, China
Understanding Big Data Analytics For Enterprise Class Hadoop And Streaming Data
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Author : IBM Paul Zikopoulos
language : en
Publisher: McGraw Hill Professional
Release Date : 2011-10-19
Understanding Big Data Analytics For Enterprise Class Hadoop And Streaming Data written by IBM Paul Zikopoulos and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-10-19 with Computers categories.
Big Data represents a new era in data exploration and utilization, and IBM is uniquely positioned to help clients navigate this transformation. This book reveals how IBM is leveraging open source Big Data technology, infused with IBM technologies, to deliver a robust, secure, highly available, enterprise-class Big Data platform. The three defining characteristics of Big Data--volume, variety, and velocity--are discussed. You'll get a primer on Hadoop and how IBM is hardening it for the enterprise, and learn when to leverage IBM InfoSphere BigInsights (Big Data at rest) and IBM InfoSphere Streams (Big Data in motion) technologies. Industry use cases are also included in this practical guide. Learn how IBM hardens Hadoop for enterprise-class scalability and reliability Gain insight into IBM's unique in-motion and at-rest Big Data analytics platform Learn tips and tricks for Big Data use cases and solutions Get a quick Hadoop primer
The Ultimate Introduction To Big Data
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Author : Frank Kane
language : en
Publisher:
Release Date : 2019
The Ultimate Introduction To Big Data written by Frank Kane and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
See it. Do it. Learn it! Businesses rely on data for decision-making, success, and survival. The volume of data companies can capture is growing every day, and big data platforms like Hadoop help store, manage, and analyze it. In The Ultimate Introduction to Big Data , big data guru Frank Kane introduces you to big data processing systems and shows you how they fit together. This liveVideo spotlights over 25 different technologies in over 14 hours of video instruction. about the subject Designed for data storage and processing, Hadoop is a reliable, fault-tolerant operating system. The most celebrated features of this open source Apache project are HDFS, Hadoop's highly-scalable distributed file system, and the MapReduce data processing engine. Together, they can process vast amounts of data across large clusters. An ecosystem of hundreds of technologies has sprung up around Hadoop to answer the ever-growing demand for large-scale data processing solutions. Understanding the architecture of massive-scale data processing applications is an increasingly important and desirable skill, and you'll have it when you complete this liveVideo course! about the video The Ultimate Introduction to Big Data teaches you how to design powerful distributed data applications. With lots of hands-on exercises, instructor Frank Kane goes beyond Hadoop to cover many related technologies, giving you valuable firsthand experience with modern data processing applications. You'll learn to choose an appropriate data storage technology for your application and discover how Hadoop clusters are managed by YARN, Tez, Mesos, and other technologies. You'll also experience the combined power of HDFS and MapReduce for storing and analyzing data at scale. Using other key parts of the Hadoop ecosystem like Hive and MySQL, you'll analyze relational data, and then tackle non-relational data analysis using HBase, Cassandra, and MongoDB. With Kafka, Sqoop, and Flume, you'll make short work of publishing data to your Hadoop cluster. When you're done, you'll have a deep understanding of data processing applications on Hadoop and its distributed systems. Inside: Using HDFS and MapReduce for storing and analyzing data at scale Analyzing relational data using Hive and MySQL Creating scripts to process data on a Hadoop cluster using Pig and Spark Using HBase, Cassandra, and MongoDB to analyze non-relational data Querying data interactively with Drill, Phoenix, and Presto Choosing an appr...
Information Technology For Manufacturing Systems Iii
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Author : Wei Deng
language : en
Publisher: Trans Tech Publications Ltd
Release Date : 2012-09-26
Information Technology For Manufacturing Systems Iii written by Wei Deng and has been published by Trans Tech Publications Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-09-26 with Technology & Engineering categories.
Selected, peer reviewed papers from the 2012 3rd International Conference on Information Technology for Manufacturing Systems (ITMS 2012), September 8-9, 2012, Qingdao, China
Developing Analytic Talent
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Author : Vincent Granville
language : en
Publisher: John Wiley & Sons
Release Date : 2014-04-07
Developing Analytic Talent written by Vincent Granville and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-04-07 with Computers categories.
Learn what it takes to succeed in the the most in-demand tech job Harvard Business Review calls it the sexiest tech job of the 21st century. Data scientists are in demand, and this unique book shows you exactly what employers want and the skill set that separates the quality data scientist from other talented IT professionals. Data science involves extracting, creating, and processing data to turn it into business value. With over 15 years of big data, predictive modeling, and business analytics experience, author Vincent Granville is no stranger to data science. In this one-of-a-kind guide, he provides insight into the essential data science skills, such as statistics and visualization techniques, and covers everything from analytical recipes and data science tricks to common job interview questions, sample resumes, and source code. The applications are endless and varied: automatically detecting spam and plagiarism, optimizing bid prices in keyword advertising, identifying new molecules to fight cancer, assessing the risk of meteorite impact. Complete with case studies, this book is a must, whether you're looking to become a data scientist or to hire one. Explains the finer points of data science, the required skills, and how to acquire them, including analytical recipes, standard rules, source code, and a dictionary of terms Shows what companies are looking for and how the growing importance of big data has increased the demand for data scientists Features job interview questions, sample resumes, salary surveys, and examples of job ads Case studies explore how data science is used on Wall Street, in botnet detection, for online advertising, and in many other business-critical situations Developing Analytic Talent: Becoming a Data Scientist is essential reading for those aspiring to this hot career choice and for employers seeking the best candidates.
Ultimate Big Data Analytics With Apache Hadoop
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Author : Simhadri Govindappa
language : en
Publisher: Orange Education Pvt Ltd
Release Date : 2024-09-09
Ultimate Big Data Analytics With Apache Hadoop written by Simhadri Govindappa and has been published by Orange Education Pvt Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-09 with Computers categories.
TAGLINE Master the Hadoop Ecosystem and Build Scalable Analytics Systems KEY FEATURES ● Explains Hadoop, YARN, MapReduce, and Tez for understanding distributed data processing and resource management. ● Delves into Apache Hive and Apache Spark for their roles in data warehousing, real-time processing, and advanced analytics. ● Provides hands-on guidance for using Python with Hadoop for business intelligence and data analytics. DESCRIPTION In a rapidly evolving Big Data job market projected to grow by 28% through 2026 and with salaries reaching up to $150,000 annually—mastering big data analytics with the Hadoop ecosystem is most sought after for career advancement. The Ultimate Big Data Analytics with Apache Hadoop is an indispensable companion offering in-depth knowledge and practical skills needed to excel in today's data-driven landscape. The book begins laying a strong foundation with an overview of data lakes, data warehouses, and related concepts. It then delves into core Hadoop components such as HDFS, YARN, MapReduce, and Apache Tez, offering a blend of theory and practical exercises. You will gain hands-on experience with query engines like Apache Hive and Apache Spark, as well as file and table formats such as ORC, Parquet, Avro, Iceberg, Hudi, and Delta. Detailed instructions on installing and configuring clusters with Docker are included, along with big data visualization and statistical analysis using Python. Given the growing importance of scalable data pipelines, this book equips data engineers, analysts, and big data professionals with practical skills to set up, manage, and optimize data pipelines, and to apply machine learning techniques effectively. Don’t miss out on the opportunity to become a leader in the big data field to unlock the full potential of big data analytics with Hadoop. WHAT WILL YOU LEARN ● Gain expertise in building and managing large-scale data pipelines with Hadoop, YARN, and MapReduce. ● Master real-time analytics and data processing with Apache Spark’s powerful features. ● Develop skills in using Apache Hive for efficient data warehousing and complex queries. ● Integrate Python for advanced data analysis, visualization, and business intelligence in the Hadoop ecosystem. ● Learn to enhance data storage and processing performance using formats like ORC, Parquet, and Delta. ● Acquire hands-on experience in deploying and managing Hadoop clusters with Docker and Kubernetes. ● Build and deploy machine learning models with tools integrated into the Hadoop ecosystem. WHO IS THIS BOOK FOR? This book is tailored for data engineers, analysts, software developers, data scientists, IT professionals, and engineering students seeking to enhance their skills in big data analytics with Hadoop. Prerequisites include a basic understanding of big data concepts, programming knowledge in Java, Python, or SQL, and basic Linux command line skills. No prior experience with Hadoop is required, but a foundational grasp of data principles and technical proficiency will help readers fully engage with the material. TABLE OF CONTENTS 1. Introduction to Hadoop and ASF 2. Overview of Big Data Analytics 3. Hadoop and YARN MapReduce and Tez 4. Distributed Query Engines: Apache Hive 5. Distributed Query Engines: Apache Spark 6. File Formats and Table Formats (Apache Ice-berg, Hudi, and Delta) 7. Python and the Hadoop Ecosystem for Big Data Analytics - BI 8. Data Science and Machine Learning with Hadoop Ecosystem 9. Introduction to Cloud Computing and Other Apache Projects Index
Big Data Processing With Hadoop
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Author : T. Revathi
language : en
Publisher: Engineering Science Reference
Release Date : 2018-11-16
Big Data Processing With Hadoop written by T. Revathi and has been published by Engineering Science Reference this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-16 with Computers categories.
Due to the increasing availability of affordable internet services, the number of users, and the need for a wider range of multimedia-based applications, internet usage is on the rise. With so many users and such a large amount of data, the requirements of analyzing large data sets leads to the need for further advancements to information processing. Big Data Processing With Hadoop is an essential reference source that discusses possible solutions for millions of users working with a variety of data applications, who expect fast turnaround responses, but encounter issues with processing data at the rate it comes in. Featuring research on topics such as market basket analytics, scheduler load simulator, and writing YARN applications, this book is ideally designed for IoT professionals, students, and engineers seeking coverage on many of the real-world challenges regarding big data.