Big Data Fundamentals
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Big Data Fundamentals
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Author : Thomas Erl
language : en
Publisher: Prentice Hall
Release Date : 2015-12-29
Big Data Fundamentals written by Thomas Erl and has been published by Prentice Hall this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-12-29 with Computers categories.
“This text should be required reading for everyone in contemporary business.” --Peter Woodhull, CEO, Modus21 “The one book that clearly describes and links Big Data concepts to business utility.” --Dr. Christopher Starr, PhD “Simply, this is the best Big Data book on the market!” --Sam Rostam, Cascadian IT Group “...one of the most contemporary approaches I’ve seen to Big Data fundamentals...” --Joshua M. Davis, PhD The Definitive Plain-English Guide to Big Data for Business and Technology Professionals Big Data Fundamentals provides a pragmatic, no-nonsense introduction to Big Data. Best-selling IT author Thomas Erl and his team clearly explain key Big Data concepts, theory and terminology, as well as fundamental technologies and techniques. All coverage is supported with case study examples and numerous simple diagrams. The authors begin by explaining how Big Data can propel an organization forward by solving a spectrum of previously intractable business problems. Next, they demystify key analysis techniques and technologies and show how a Big Data solution environment can be built and integrated to offer competitive advantages. Discovering Big Data’s fundamental concepts and what makes it different from previous forms of data analysis and data science Understanding the business motivations and drivers behind Big Data adoption, from operational improvements through innovation Planning strategic, business-driven Big Data initiatives Addressing considerations such as data management, governance, and security Recognizing the 5 “V” characteristics of datasets in Big Data environments: volume, velocity, variety, veracity, and value Clarifying Big Data’s relationships with OLTP, OLAP, ETL, data warehouses, and data marts Working with Big Data in structured, unstructured, semi-structured, and metadata formats Increasing value by integrating Big Data resources with corporate performance monitoring Understanding how Big Data leverages distributed and parallel processing Using NoSQL and other technologies to meet Big Data’s distinct data processing requirements Leveraging statistical approaches of quantitative and qualitative analysis Applying computational analysis methods, including machine learning
Azure Data Fundamentals
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Author : Michael John Pena
language : en
Publisher: O'Reilly Media
Release Date : 2025-11-30
Azure Data Fundamentals written by Michael John Pena and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-11-30 with Computers categories.
Learn the essential skills and concepts for working with data in the cloud using Microsoft Azure. With this practical guide, professionals new to data management and Azure will learn how to leverage Azure services such as Azure Cosmos DB, Azure Storage, Azure SQL, and Microsoft Fabric to create, store, process, analyze, and visualize data. Author Michael John Pena, data and AI director at Playtime Solutions, also shows you how to apply security, monitoring, and optimization techniques to your data solutions in Azure. Ideal as a resource for the Azure Data Fundamentals certification, this book also provides knowledge you can apply in your daily work even after platforms evolve and new technologies emerge. You'll gain insightful experience that will help you apply the fundamentals of data operations and engineering in the Azure ecosystem. Learn core data concepts and correlating Microsoft Azure data services Discover how to work with relational and nonrelational data on Azure Build data analytics solutions using various Azure data services Identify and describe core data workloads and data analytics workloads on Azure Describe how to work with data security and privacy on Azure Explain the use cases, benefits, and limitations of different Azure data services
Fundamentals Of Big Data Analytics
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Author : Dr.T.Vijaya Saradhi
language : en
Publisher: GCS PUBLISHERS
Release Date : 2022-05-02
Fundamentals Of Big Data Analytics written by Dr.T.Vijaya Saradhi and has been published by GCS PUBLISHERS this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-02 with Antiques & Collectibles categories.
Fundamentals of Big Data Analytics written by Dr.Thomman Vijaya SaradhiDr. Syed Azahad Mr .Sreejith R, Dr. Sreekumar Narayanan
The Fundamentals Of Data Science Big Data Deep Learning And Machine Learning What You Need To Know About Data Science And Why It Matters
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Author : Vlad Sozonov
language : en
Publisher: Vinco Publishing
Release Date : 2019-11-21
The Fundamentals Of Data Science Big Data Deep Learning And Machine Learning What You Need To Know About Data Science And Why It Matters written by Vlad Sozonov and has been published by Vinco Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-21 with Computers categories.
Data science is no easy term to define. While there are many definitions available that point out its statistical or logical aspects, others focus on its machine learning impacts. Today only, get this Amazon book for just $19.99 for a limited time. Regularly priced at $35.99. The truth is, data science is a process that requires an understanding of multiple fields, methods, techniques, and more. Data science cannot be easily labeled because, when applied, it looks different to each person, business, or organization utilizing it. While the term may not be easy to define, what it is used for, can be used for, and approaches to it can be more easily understood. And that is precisely what this book aims to do. Scroll Up & Click to Buy Now! Here Is A Preview Of What You'll Discover...In this step-by-step book: This book will not only thoroughly go over all the skills, people, and steps involved in data science, it will also look closely at: ● What big data is and how data science came from it. ● How data has evolved, resulting in new methods for understanding it. ● How data science influenced artificial intelligence. ● How data science is used in machine learning and deep learning. ● How data science revolutionizes the way we train machines and set up neural networks. Data science, big data, machine learning, and deep learning tend to intimidate people. Many believe it is too complicated or technology-centered for them to break into these fields. This book is designed to simplify these complex areas in a way that anyone can understand the fundamentals. Whether you are just hearing about data science, are a student studying it in college, or looking to expand your career, this book has something to offer every type of data enthusiast. Order your copy today! Take action right away by purchase this book "The Fundamentals of Data Science: Big Data, Deep Learning, and Machine Learning: What you need to know about data science and why it matters.", for a limited time discount of only $19.99! Hurry Up!! Tags: ● data science quick ● data science strategy ● data science trading ● data science journal ● insight data science ● data science salary ● data science jobs ● data science espanol ● data science case study ● data science beginner guide
Understanding Big Data Scalability
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Author : Cory Isaacson
language : en
Publisher: Prentice Hall
Release Date : 2014-07-11
Understanding Big Data Scalability written by Cory Isaacson and has been published by Prentice Hall this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-07-11 with Computers categories.
Get Started Scaling Your Database Infrastructure for High-Volume Big Data Applications “Understanding Big Data Scalability presents the fundamentals of scaling databases from a single node to large clusters. It provides a practical explanation of what ‘Big Data’ systems are, and fundamental issues to consider when optimizing for performance and scalability. Cory draws on many years of experience to explain issues involved in working with data sets that can no longer be handled with single, monolithic relational databases.... His approach is particularly relevant now that relational data models are making a comeback via SQL interfaces to popular NoSQL databases and Hadoop distributions.... This book should be especially useful to database practitioners new to scaling databases beyond traditional single node deployments.” —Brian O’Krafka, software architect Understanding Big Data Scalability presents a solid foundation for scaling Big Data infrastructure and helps you address each crucial factor associated with optimizing performance in scalable and dynamic Big Data clusters. Database expert Cory Isaacson offers practical, actionable insights for every technical professional who must scale a database tier for high-volume applications. Focusing on today’s most common Big Data applications, he introduces proven ways to manage unprecedented data growth from widely diverse sources and to deliver real-time processing at levels that were inconceivable until recently. Isaacson explains why databases slow down, reviews each major technique for scaling database applications, and identifies the key rules of database scalability that every architect should follow. You’ll find insights and techniques proven with all types of database engines and environments, including SQL, NoSQL, and Hadoop. Two start-to-finish case studies walk you through planning and implementation, offering specific lessons for formulating your own scalability strategy. Coverage includes Understanding the true causes of database performance degradation in today’s Big Data environments Scaling smoothly to petabyte-class databases and beyond Defining database clusters for maximum scalability and performance Integrating NoSQL or columnar databases that aren’t “drop-in” replacements for RDBMSes Scaling application components: solutions and options for each tier Recognizing when to scale your data tier—a decision with enormous consequences for your application environment Why data relationships may be even more important in non-relational databases Why virtually every database scalability implementation still relies on sharding, and how to choose the best approach How to set clear objectives for architecting high-performance Big Data implementations The Big Data Scalability Series is a comprehensive, four-part series, containing information on many facets of database performance and scalability. Understanding Big Data Scalability is the first book in the series. Learn more and join the conversation about Big Data scalability at bigdatascalability.com.
Big Data Fundamentals A Comprehensive Guide To Data Management And Analysis
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Author : Dhruvitkumar Patel
language : en
Publisher: Academic Guru Publishing House
Release Date : 2024-08-27
Big Data Fundamentals A Comprehensive Guide To Data Management And Analysis written by Dhruvitkumar Patel and has been published by Academic Guru Publishing House this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-27 with Study Aids categories.
“Big Data Fundamentals: A Comprehensive Guide to Data Management and Analysis” aims to provide a complete knowledge of the basic ideas and methods related with big data. Ten thorough chapters, each with an emphasis on important aspects of big data including definition, features, technologies, processing methods, and security issues, make up the book. The book starts with an overview of big data and its impact on modern businesses then looks at many technologies and tools within the big data ecosystem. Readers will learn recommended practices for data management as well as data storage options—including data lakes and cloud storage. Discussions on data processing and analytics include batch and stream processing as well as methods like ETL. Emphasizing their importance in obtaining insightful analysis from large volumes of data, the book also investigates the junction of data mining and machine learning. It underlines the need for data visualization for efficient result interpretation and successful result communication. Analyzed are the security and privacy issues in the framework of big data, along with solutions for guaranteeing compliance and governance. The book presents advanced topics like real-time processing and future trends along with useful implementation techniques. This comprehensive handbook is a vital tool for anybody wishing to understand and use big data’s potential in their career.
Google Bigquery Analytics
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Author : Jordan Tigani
language : en
Publisher: John Wiley & Sons
Release Date : 2014-06-09
Google Bigquery Analytics written by Jordan Tigani 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 2014-06-09 with Computers categories.
How to effectively use BigQuery, avoid common mistakes, and execute sophisticated queries against large datasets Google BigQuery Analytics is the perfect guide for business and data analysts who want the latest tips on running complex queries and writing code to communicate with the BigQuery API. The book uses real-world examples to demonstrate current best practices and techniques, and also explains and demonstrates streaming ingestion, transformation via Hadoop in Google Compute engine, AppEngine datastore integration, and using GViz with Tableau to generate charts of query results. In addition to the mechanics of BigQuery, the book also covers the architecture of the underlying Dremel query engine, providing a thorough understanding that leads to better query results. Features a companion website that includes all code and data sets from the book Uses real-world examples to explain everything analysts need to know to effectively use BigQuery Includes web application examples coded in Python
Fundamentals Of Database Systems
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Author : Ramez Elmasri
language : en
Publisher:
Release Date : 2007
Fundamentals Of Database Systems written by Ramez Elmasri and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Database management categories.
This edition combines clear explanations of database theory and design with up-to-date coverage of models and real systems. It features excellent examples and access to Addison Wesley's database Web site that includes further teaching, tutorials and many useful student resources.
Big Data In Cardiology Predicting Preventing And Managing Diseases
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Author : Bikal Dhungel
language : en
Publisher: GRIN Verlag
Release Date : 2020-10-28
Big Data In Cardiology Predicting Preventing And Managing Diseases written by Bikal Dhungel and has been published by GRIN Verlag this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-28 with Medical categories.
Master's Thesis from the year 2020 in the subject Health Sciences - Health Logistics, grade: 1,7, Linnaeus University (School of Informatics), course: Information Systems, language: English, abstract: This study was conducted to analyze this process closer focusing on a case of Cardiology. Conducting a comprehensive literature review and qualitative expert interviews, the impact of big data in the field of Cardiology was explored. The result of the study shows that big data can play a positive role in three aspects: prediction of disease, prevention of disease and management of disease. Big data enables us to build models that can be used to predict the occurrence of disease. Based on this information, actions can be taken to prevent the disease. Data also helps to manage the disease by offering helpful insights. Medical personnel can retrieve the patient data, with the help of AI, they can make faster decisions allowing them to spend more quality time with the patients and reduce cognitive errors. Through the interviews, it was understood that even though the positive role of big data has been acknowledged, the implementation is still a challenge due to various limitations. The challenges lie mainly on technical know-how and domain knowledge. Further challenges were data security and privacy issues that need to be addressed to mitigate the risks that can be caused by them. The examples of big data implementation in various cases like in heart failure prediction or prevention shows a positive picture. The overwhelming majority of case studies analyzed in this regard show an optimistic picture. Due to growing importance and use of smart devices, IoT, genomics and the recent developments in the field of ICTs, it is expected that big data will not only leave a positive influence on the field of Cardiology, it will also change the way medicine is practiced and healthcare is offered. The statement ‘Data is the new oil’ has been broadly acknowledged due to its wide-ranging importance. Utilizing big data offers a variety of benefits. Although the health sector was late in terms of exploiting the benefits of big data, currently, the adoption is accelerating. Healthcare is increasingly becoming an information science and the implementation of electronic medical records (EMR) and other information systems is growing rapidly. The patient data originating from smart devices and other sources like genomic databases are supporting the healthcare sector offering better healthcare delivery and increasing efficiency, hence saving costs.
Fundamentals Of Data Science
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Author : Sanjeev J. Wagh
language : en
Publisher: CRC Press
Release Date : 2021-09-26
Fundamentals Of Data Science written by Sanjeev J. Wagh and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-26 with Business & Economics categories.
Fundamentals of Data Science is designed for students, academicians and practitioners with a complete walkthrough right from the foundational groundwork required to outlining all the concepts, techniques and tools required to understand Data Science. Data Science is an umbrella term for the non-traditional techniques and technologies that are required to collect, aggregate, process, and gain insights from massive datasets. This book offers all the processes, methodologies, various steps like data acquisition, pre-process, mining, prediction, and visualization tools for extracting insights from vast amounts of data by the use of various scientific methods, algorithms, and processes Readers will learn the steps necessary to create the application with SQl, NoSQL, Python, R, Matlab, Octave and Tablue. This book provides a stepwise approach to building solutions to data science applications right from understanding the fundamentals, performing data analytics to writing source code. All the concepts are discussed in simple English to help the community to become Data Scientist without much pre-requisite knowledge. Features : Simple strategies for developing statistical models that analyze data and detect patterns, trends, and relationships in data sets. Complete roadmap to Data Science approach with dedicatedsections which includes Fundamentals, Methodology and Tools. Focussed approach for learning and practice various Data Science Toolswith Sample code and examples for practice. Information is presented in an accessible way for students, researchers and academicians and professionals.