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5 books on Big Data [PDF]
October 31, 2024 | 25 |
These books are covering big data storage solutions, analytics frameworks, machine learning algorithms, data visualization techniques, distributed computing, data governance and real-time processing.
1. The Enterprise Big Data Lake: Delivering the Promise of Big Data and Data Science
2019 by Alex Gorelik
Embark on a revolutionary journey into the realm of big data technology with "The Enterprise Big Data Lake: Delivering the Promise of Big Data and Data Science." This book challenges the conventional methods by introducing the daring concept of a data lake, designed to unleash the full potential of big data technology and offer convenient self-service capabilities. Drawing on insights from discussions with practitioners and executives across a diverse spectrum of organizations, including tech giants like Google, LinkedIn, and Facebook, as well as government entities and traditional corporations, the book provides a comprehensive understanding of what a data lake is, why enterprises require one, and how to successfully build and implement it using best practices. Authored by Alex Gorelik, CTO, and founder of Waterline Data, the book elucidates why outdated systems and processes can no longer meet the evolving data needs of enterprises. Through a collection of essays on data lake implementation, readers gain valuable insights into initiatives, analytic projects, experiences, and best practices from data experts in various industries. The book also offers a succinct introduction to data warehousing, big data, and data science, guiding readers through different paths enterprises take to build a data lake, exploring self-service models, and providing architects with diverse methods for successful implementation based on real-world experiences.
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2. Data Lake for Enterprises
2017 by Tomcy John, Pankaj Misra
"Data Lake for Enterprises" serves as a hands-on guide for Java developers and architects aiming to implement an enterprise data lake, using the Lambda Architecture as the foundational framework. This comprehensive book explores the core concepts of data lakes, emphasizing their significance in modern business strategies, and provides practical insights into building enterprise-level data lakes using popular big data technologies such as Apache Hadoop, Spark, Sqoop, Flume, and ElasticSearch. Divided into three main sections, the book introduces the concepts of data lakes and the Lambda architecture, explores the key components of building a data lake, and offers a practical demonstration of implementation through real-world use cases. Whether you are seeking to gain hands-on experience with Lambda Architecture and big data technologies or are keen on implementing a practical solution for your enterprise, this book guides you through the process, helping you choose the right technologies and patterns to build an efficient and effective enterprise data lake. The pragmatic approach of the book makes it a valuable resource for those looking to leverage big data technologies and Lambda Architecture in the construction of a robust data lake.
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3. Enterprise Big Data Engineering, Analytics, and Management
2016 by Atzmueller, Martin
Witness the transformative power of big data in decision-making processes with "Enterprise Big Data Engineering, Analytics, and Management." This book highlights the crucial role of big data in forecasting and predictive analytics, showcasing its ability to construct a comprehensive enterprise overview through the retrospective analysis of vast data sets. As the volume of data continues to surge, the demand for innovative methods to analyze, comprehend, and harness the potential of big data becomes imperative. "Enterprise Big Data Engineering, Analytics, and Management" introduces pioneering methodologies and practical approaches tailored for engineering, managing, and analyzing large-scale data sets, with a specific focus on their application in enterprise contexts. Exploring fundamental big data concepts such as data mining, artificial intelligence, and information extraction, this publication serves as a platform for reshaping current research in the field. It is an indispensable resource for data analysts, IT professionals, researchers, and graduate-level students seeking to deepen their understanding of big data and its applications.
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4. Big Data Imperatives: Enterprise ‘Big Data’ Warehouse, ‘BI’ Implementations and Analytics
2013 by Soumendra Mohanty, Madhu Jagadeesh, Harsha Srivatsa
"Big Data Imperatives: Enterprise ‘Big Data’ Warehouse, ‘BI’ Implementations and Analytics" delves into the essential questions surrounding big data, guiding readers through critical considerations such as identifying the relevant data, assessing the volume required for justification, determining optimal processing methods, and establishing the duration of data activation for analysis, marketing, and business intelligence applications. As big data transitions from isolated projects to mainstream business integration, the book emphasizes that the true value lies not only in its sheer size but in its effective utilization. Addressing characteristics such as vast, distributed aggregations of loosely structured and incomplete data, petabytes/exabytes of information, and the involvement of millions/billions of contributors, the book explores the complementary relationship between traditional data warehouses and big-data analytics platforms. It emphasizes the ability of big data to process massive records faster and more cost-effectively, offering a platform for comprehensive and reliable data analysis focused on specific business capabilities. Serving as a handbook for practitioners, the book covers methodology, technical architecture, analytics techniques, and best practices, making it valuable for IT professionals, data warehousing and business intelligence experts, data analysts, architects, developers, and business users alike. Whether seasoned professionals or newcomers to big data, readers will gain a profound understanding of the technology, big data platform implementation, and its application in analytics, along with insights into architectures, design patterns, and implementation best practices."
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5. Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data
2011 by Paul Zikopoulos, Chris Eaton
Explore the realm of Big Data with "Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data," where IBM takes center stage in guiding readers through this transformative landscape. Unveiling the fusion of open-source Big Data technology with IBM's innovations, the book illuminates the creation of a robust, secure, and highly available enterprise-class Big Data platform. Delving into the three defining characteristics of Big Data—volume, variety, and velocity—it provides a comprehensive overview of Hadoop, emphasizing how IBM fortifies it for enterprise applications. Readers discover the strategic implementation of IBM InfoSphere BigInsights for data at rest and IBM InfoSphere Streams for data in motion. The book not only imparts essential knowledge about IBM's unique in-motion and at-rest Big Data analytics platform but also offers practical insights through industry use cases. Whether unraveling the intricacies of Hadoop for enterprise-class scalability or gaining valuable tips and tricks for various Big Data use cases, this guide equips readers for the evolving landscape of data analytics.
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How to download PDF:
1. Install Google Books Downloader
2. Enter Book ID to the search box and press Enter
3. Click "Download Book" icon and select PDF*
* - note that for yellow books only preview pages are downloaded
1. The Enterprise Big Data Lake: Delivering the Promise of Big Data and Data Science
2019 by Alex Gorelik
Embark on a revolutionary journey into the realm of big data technology with "The Enterprise Big Data Lake: Delivering the Promise of Big Data and Data Science." This book challenges the conventional methods by introducing the daring concept of a data lake, designed to unleash the full potential of big data technology and offer convenient self-service capabilities. Drawing on insights from discussions with practitioners and executives across a diverse spectrum of organizations, including tech giants like Google, LinkedIn, and Facebook, as well as government entities and traditional corporations, the book provides a comprehensive understanding of what a data lake is, why enterprises require one, and how to successfully build and implement it using best practices. Authored by Alex Gorelik, CTO, and founder of Waterline Data, the book elucidates why outdated systems and processes can no longer meet the evolving data needs of enterprises. Through a collection of essays on data lake implementation, readers gain valuable insights into initiatives, analytic projects, experiences, and best practices from data experts in various industries. The book also offers a succinct introduction to data warehousing, big data, and data science, guiding readers through different paths enterprises take to build a data lake, exploring self-service models, and providing architects with diverse methods for successful implementation based on real-world experiences.
Download PDF
2. Data Lake for Enterprises
2017 by Tomcy John, Pankaj Misra
"Data Lake for Enterprises" serves as a hands-on guide for Java developers and architects aiming to implement an enterprise data lake, using the Lambda Architecture as the foundational framework. This comprehensive book explores the core concepts of data lakes, emphasizing their significance in modern business strategies, and provides practical insights into building enterprise-level data lakes using popular big data technologies such as Apache Hadoop, Spark, Sqoop, Flume, and ElasticSearch. Divided into three main sections, the book introduces the concepts of data lakes and the Lambda architecture, explores the key components of building a data lake, and offers a practical demonstration of implementation through real-world use cases. Whether you are seeking to gain hands-on experience with Lambda Architecture and big data technologies or are keen on implementing a practical solution for your enterprise, this book guides you through the process, helping you choose the right technologies and patterns to build an efficient and effective enterprise data lake. The pragmatic approach of the book makes it a valuable resource for those looking to leverage big data technologies and Lambda Architecture in the construction of a robust data lake.
Download PDF
3. Enterprise Big Data Engineering, Analytics, and Management
2016 by Atzmueller, Martin
Witness the transformative power of big data in decision-making processes with "Enterprise Big Data Engineering, Analytics, and Management." This book highlights the crucial role of big data in forecasting and predictive analytics, showcasing its ability to construct a comprehensive enterprise overview through the retrospective analysis of vast data sets. As the volume of data continues to surge, the demand for innovative methods to analyze, comprehend, and harness the potential of big data becomes imperative. "Enterprise Big Data Engineering, Analytics, and Management" introduces pioneering methodologies and practical approaches tailored for engineering, managing, and analyzing large-scale data sets, with a specific focus on their application in enterprise contexts. Exploring fundamental big data concepts such as data mining, artificial intelligence, and information extraction, this publication serves as a platform for reshaping current research in the field. It is an indispensable resource for data analysts, IT professionals, researchers, and graduate-level students seeking to deepen their understanding of big data and its applications.
Download PDF
4. Big Data Imperatives: Enterprise ‘Big Data’ Warehouse, ‘BI’ Implementations and Analytics
2013 by Soumendra Mohanty, Madhu Jagadeesh, Harsha Srivatsa
"Big Data Imperatives: Enterprise ‘Big Data’ Warehouse, ‘BI’ Implementations and Analytics" delves into the essential questions surrounding big data, guiding readers through critical considerations such as identifying the relevant data, assessing the volume required for justification, determining optimal processing methods, and establishing the duration of data activation for analysis, marketing, and business intelligence applications. As big data transitions from isolated projects to mainstream business integration, the book emphasizes that the true value lies not only in its sheer size but in its effective utilization. Addressing characteristics such as vast, distributed aggregations of loosely structured and incomplete data, petabytes/exabytes of information, and the involvement of millions/billions of contributors, the book explores the complementary relationship between traditional data warehouses and big-data analytics platforms. It emphasizes the ability of big data to process massive records faster and more cost-effectively, offering a platform for comprehensive and reliable data analysis focused on specific business capabilities. Serving as a handbook for practitioners, the book covers methodology, technical architecture, analytics techniques, and best practices, making it valuable for IT professionals, data warehousing and business intelligence experts, data analysts, architects, developers, and business users alike. Whether seasoned professionals or newcomers to big data, readers will gain a profound understanding of the technology, big data platform implementation, and its application in analytics, along with insights into architectures, design patterns, and implementation best practices."
Download PDF
5. Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data
2011 by Paul Zikopoulos, Chris Eaton
Explore the realm of Big Data with "Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data," where IBM takes center stage in guiding readers through this transformative landscape. Unveiling the fusion of open-source Big Data technology with IBM's innovations, the book illuminates the creation of a robust, secure, and highly available enterprise-class Big Data platform. Delving into the three defining characteristics of Big Data—volume, variety, and velocity—it provides a comprehensive overview of Hadoop, emphasizing how IBM fortifies it for enterprise applications. Readers discover the strategic implementation of IBM InfoSphere BigInsights for data at rest and IBM InfoSphere Streams for data in motion. The book not only imparts essential knowledge about IBM's unique in-motion and at-rest Big Data analytics platform but also offers practical insights through industry use cases. Whether unraveling the intricacies of Hadoop for enterprise-class scalability or gaining valuable tips and tricks for various Big Data use cases, this guide equips readers for the evolving landscape of data analytics.
Download PDF
How to download PDF:
1. Install Google Books Downloader
2. Enter Book ID to the search box and press Enter
3. Click "Download Book" icon and select PDF*
* - note that for yellow books only preview pages are downloaded
