Principles of Database Management PDF Download
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Author: Wilfried Lemahieu
Publisher: Cambridge University Press
ISBN: 1107186129
Category : Computers
Languages : en
Pages : 817
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Book Description
Introductory, theory-practice balanced text teaching the fundamentals of databases to advanced undergraduates or graduate students in information systems or computer science.
Author: Wilfried Lemahieu
Publisher: Cambridge University Press
ISBN: 1107186129
Category : Computers
Languages : en
Pages : 817
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Book Description
Introductory, theory-practice balanced text teaching the fundamentals of databases to advanced undergraduates or graduate students in information systems or computer science.
Author: Brij B Gupta
Publisher: World Scientific
ISBN: 9811257132
Category : Computers
Languages : en
Pages : 288
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Book Description
With the proliferation of information, big data management and analysis have become an indispensable part of any system to handle such amounts of data. The amount of data generated by the multitude of interconnected devices increases exponentially, making the storage and processing of these data a real challenge.Big data management and analytics have gained momentum in almost every industry, ranging from finance or healthcare. Big data can reveal key insights if handled and analyzed properly; it has great application potential to improve the working of any industry. This book covers the spectrum aspects of big data; from the preliminary level to specific case studies. It will help readers gain knowledge of the big data landscape.Highlights of the topics covered include description of the Big Data ecosystem; real-world instances of big data issues; how the Vs of Big Data (volume, velocity, variety, veracity, valence, and value) affect data collection, monitoring, storage, analysis, and reporting; structural process to get value out of Big Data and recognize the differences between a standard database management system and a big data management system.Readers will gain insights into choice of data models, data extraction, data integration to solve large data problems, data modelling using machine learning techniques, Spark's scalable machine learning techniques, modeling a big data problem into a graph database and performing scalable analytical operations over the graph and different tools and techniques for processing big data and its applications including in healthcare and finance.
Author: Singh, Manoj Kumar
Publisher: IGI Global
ISBN: 1522501835
Category : Computers
Languages : en
Pages : 345
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Book Description
“Big data” has become a commonly used term to describe large-scale and complex data sets which are difficult to manage and analyze using standard data management methodologies. With applications across sectors and fields of study, the implementation and possible uses of big data are limitless. Effective Big Data Management and Opportunities for Implementation explores emerging research on the ever-growing field of big data and facilitates further knowledge development on methods for handling and interpreting large data sets. Providing multi-disciplinary perspectives fueled by international research, this publication is designed for use by data analysts, IT professionals, researchers, and graduate-level students interested in learning about the latest trends and concepts in big data.
Author: Conor Suarez
Publisher:
ISBN: 9781682851807
Category : Big data
Languages : en
Pages : 0
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Book Description
Big data is a broad term that defines the structured or unstructured data sets. It is a challenging field which includes analysis, transfer and visualization of data. This book presents in-depth description of various dimensions of big data analysis like complexity and variability. It elucidates the handling and storing of data through database management systems. It examines the challenges involved in big data analysis including data mining tools and techniques. Students and professionals engaged in this field will find this book beneficial.
Author: James Warren
Publisher: Simon and Schuster
ISBN: 1638351104
Category : Computers
Languages : en
Pages : 481
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Book Description
Summary Big Data teaches you to build big data systems using an architecture that takes advantage of clustered hardware along with new tools designed specifically to capture and analyze web-scale data. It describes a scalable, easy-to-understand approach to big data systems that can be built and run by a small team. Following a realistic example, this book guides readers through the theory of big data systems, how to implement them in practice, and how to deploy and operate them once they're built. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Book Web-scale applications like social networks, real-time analytics, or e-commerce sites deal with a lot of data, whose volume and velocity exceed the limits of traditional database systems. These applications require architectures built around clusters of machines to store and process data of any size, or speed. Fortunately, scale and simplicity are not mutually exclusive. Big Data teaches you to build big data systems using an architecture designed specifically to capture and analyze web-scale data. This book presents the Lambda Architecture, a scalable, easy-to-understand approach that can be built and run by a small team. You'll explore the theory of big data systems and how to implement them in practice. In addition to discovering a general framework for processing big data, you'll learn specific technologies like Hadoop, Storm, and NoSQL databases. This book requires no previous exposure to large-scale data analysis or NoSQL tools. Familiarity with traditional databases is helpful. What's Inside Introduction to big data systems Real-time processing of web-scale data Tools like Hadoop, Cassandra, and Storm Extensions to traditional database skills About the Authors Nathan Marz is the creator of Apache Storm and the originator of the Lambda Architecture for big data systems. James Warren is an analytics architect with a background in machine learning and scientific computing. Table of Contents A new paradigm for Big Data PART 1 BATCH LAYER Data model for Big Data Data model for Big Data: Illustration Data storage on the batch layer Data storage on the batch layer: Illustration Batch layer Batch layer: Illustration An example batch layer: Architecture and algorithms An example batch layer: Implementation PART 2 SERVING LAYER Serving layer Serving layer: Illustration PART 3 SPEED LAYER Realtime views Realtime views: Illustration Queuing and stream processing Queuing and stream processing: Illustration Micro-batch stream processing Micro-batch stream processing: Illustration Lambda Architecture in depth
Author: Yusuf Aytas
Publisher: John Wiley & Sons
ISBN: 1119690927
Category : Mathematics
Languages : en
Pages : 338
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Book Description
DESIGNING BIG DATA PLATFORMS Provides expert guidance and valuable insights on getting the most out of Big Data systems An array of tools are currently available for managing and processing data—some are ready-to-go solutions that can be immediately deployed, while others require complex and time-intensive setups. With such a vast range of options, choosing the right tool to build a solution can be complicated, as can determining which tools work well with each other. Designing Big Data Platforms provides clear and authoritative guidance on the critical decisions necessary for successfully deploying, operating, and maintaining Big Data systems. This highly practical guide helps readers understand how to process large amounts of data with well-known Linux tools and database solutions, use effective techniques to collect and manage data from multiple sources, transform data into meaningful business insights, and much more. Author Yusuf Aytas, a software engineer with a vast amount of big data experience, discusses the design of the ideal Big Data platform: one that meets the needs of data analysts, data engineers, data scientists, software engineers, and a spectrum of other stakeholders across an organization. Detailed yet accessible chapters cover key topics such as stream data processing, data analytics, data science, data discovery, and data security. This real-world manual for Big Data technologies: Provides up-to-date coverage of the tools currently used in Big Data processing and management Offers step-by-step guidance on building a data pipeline, from basic scripting to distributed systems Highlights and explains how data is processed at scale Includes an introduction to the foundation of a modern data platform Designing Big Data Platforms: How to Use, Deploy, and Maintain Big Data Systems is a must-have for all professionals working with Big Data, as well researchers and students in computer science and related fields.
Author: Mr. S. Sureshkumar
Publisher: AG PUBLISHING HOUSE (AGPH Books)
ISBN: 9395936053
Category : Study Aids
Languages : en
Pages : 223
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Book Description
A database is a collection of data that are connected. Databases allow for the efficient retrieval, insertion, and deletion of data from the database. Additionally, databases arrange the data in the form of tables, views, schemas, reports, and other such things. For instance, a university database would categorize the data on students, teachers, and administrative staff, among other categories, which will aid in the effective retrieval, insertion, and deletion of data from the database. The database management system (DBMS) is in charge of managing the data; the database engine enables users to access, lock, and modify data; and the database schema outlines the logical structure of the database. These three fundamental components assist ensure concurrency, security, the integrity of data, and standardized methods for the administration of data. The database management system provides support for a wide variety of duties that are often associated with database administration. These tasks include change management, performance monitoring and tuning, security, backup and recovery, and more. The majority of database management systems are also responsible for automatic rollbacks and restarts, as well as the recording and auditing of activity in databases and the applications that use them. Other responsibilities of these systems include logging and auditing database activity. A centralized view of the data is provided by the DBMS. This view may be accessed in a controlled way by numerous users from various places at the same time. A database management system (DBMS) may restrict the data that end users see and how they see the data, offering many perspectives on a single database structure. Because the DBMS processes all requests, end users and software programs do not need to be aware of where the data is physically located or on what kind of storage media it is stored because the DBMS does all of the work for them. This book contains chapters and topics that cover all of the necessary information that is associated with “Data management system”. After doing a great deal of study on the subject, the author decided to add the content that is now included in this book. After engaging in a great deal of conversation, the writers of this book contributed all of the material that is included in this book. This book contains a lot of material that will assist readers in gaining a better understanding of all the chapters.
Author: Atzmueller, Martin
Publisher: IGI Global
ISBN: 1522502947
Category : Computers
Languages : en
Pages : 293
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Book Description
The significance of big data can be observed in any decision-making process as it is often used for forecasting and predictive analytics. Additionally, big data can be used to build a holistic view of an enterprise through a collection and analysis of large data sets retrospectively. As the data deluge deepens, new methods for analyzing, comprehending, and making use of big data become necessary. Enterprise Big Data Engineering, Analytics, and Management presents novel methodologies and practical approaches to engineering, managing, and analyzing large-scale data sets with a focus on enterprise applications and implementation. Featuring essential big data concepts including data mining, artificial intelligence, and information extraction, this publication provides a platform for retargeting the current research available in the field. Data analysts, IT professionals, researchers, and graduate-level students will find the timely research presented in this publication essential to furthering their knowledge in the field.
Author: David Loshin
Publisher: Elsevier
ISBN: 0124186645
Category : Computers
Languages : en
Pages : 143
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Book Description
Big Data Analytics will assist managers in providing an overview of the drivers for introducing big data technology into the organization and for understanding the types of business problems best suited to big data analytics solutions, understanding the value drivers and benefits, strategic planning, developing a pilot, and eventually planning to integrate back into production within the enterprise. Guides the reader in assessing the opportunities and value proposition Overview of big data hardware and software architectures Presents a variety of technologies and how they fit into the big data ecosystem
Author: Soumendra Mohanty
Publisher: Apress
ISBN: 1430248726
Category : Computers
Languages : en
Pages : 311
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Book Description
Big Data Imperatives, focuses on resolving the key questions on everyone's mind: Which data matters? Do you have enough data volume to justify the usage? How you want to process this amount of data? How long do you really need to keep it active for your analysis, marketing, and BI applications? Big data is emerging from the realm of one-off projects to mainstream business adoption; however, the real value of big data is not in the overwhelming size of it, but more in its effective use. Big Data Imperatives describes the complementary nature of traditional data warehouses and big-data analytics platforms and how they feed each other. This book aims to bring the big data and analytics realms together with a greater focus on architectures that leverage the scale and power of big data and the ability to integrate and apply analytics principles to data which earlier was not accessible. This book can also be used as a handbook for practitioners; helping them on methodology,technical architecture, analytics techniques and best practices. At the same time, this book intends to hold the interest of those new to big data and analytics by giving them a deep insight into the realm of big data.
