Data Warehousing And Analytics


Data Warehousing And Analytics
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Data Warehousing And Analytics


Data Warehousing And Analytics
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Author : David Taniar
language : en
Publisher: Springer Nature
Release Date : 2022-02-04

Data Warehousing And Analytics written by David Taniar and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-04 with Computers categories.


This textbook covers all central activities of data warehousing and analytics, including transformation, preparation, aggregation, integration, and analysis. It discusses the full spectrum of the journey of data from operational/transactional databases, to data warehouses and data analytics; as well as the role that data warehousing plays in the data processing lifecycle. It also explains in detail how data warehouses may be used by data engines, such as BI tools and analytics algorithms to produce reports, dashboards, patterns, and other useful information and knowledge. The book is divided into six parts, ranging from the basics of data warehouse design (Part I - Star Schema, Part II - Snowflake and Bridge Tables, Part III - Advanced Dimensions, and Part IV - Multi-Fact and Multi-Input), to more advanced data warehousing concepts (Part V - Data Warehousing and Evolution) and data analytics (Part VI - OLAP, BI, and Analytics). This textbook approaches data warehousing from the case study angle. Each chapter presents one or more case studies to thoroughly explain the concepts and has different levels of difficulty, hence learning is incremental. In addition, every chapter has also a section on further readings which give pointers and references to research papers related to the chapter. All these features make the book ideally suited for either introductory courses on data warehousing and data analytics, or even for self-studies by professionals. The book is accompanied by a web page that includes all the used datasets and codes as well as slides and solutions to exercises.



Big Data Imperatives


Big Data Imperatives
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Author : Soumendra Mohanty
language : en
Publisher: Apress
Release Date : 2013-06-24

Big Data Imperatives written by Soumendra Mohanty and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-24 with Computers categories.


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.



Data Warehousing And Analytics


Data Warehousing And Analytics
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Author : David Taniar
language : en
Publisher: Springer
Release Date : 2022-03-06

Data Warehousing And Analytics written by David Taniar and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-06 with Computers categories.


This textbook covers all central activities of data warehousing and analytics, including transformation, preparation, aggregation, integration, and analysis. It discusses the full spectrum of the journey of data from operational/transactional databases, to data warehouses and data analytics; as well as the role that data warehousing plays in the data processing lifecycle. It also explains in detail how data warehouses may be used by data engines, such as BI tools and analytics algorithms to produce reports, dashboards, patterns, and other useful information and knowledge. The book is divided into six parts: “Part I – Star Schema” describes the foundation of data warehouse design. “Part II – Snowflake and Bridge Tables” then expands the concept of a simple star schema by introducing the concept of hierarchy, bridge tables, as well as the use of bridge tables in temporal data warehousing. “Part III – Advanced Dimensions” elaborates various dimension models, namely determinant dimensions, junk dimensions, dimension keys, and one-attribute dimensions, which all enrich the semantics of the star schema. “Part IV – Multi-Fact and Multi-Input” introduces multi-fact star schemas, where the star schema has multi-fact entities. A multi-fact can also be created by slicing one fact into multi-facts, which is discussed next. Eventually the creation of a star schema is introduced where an operational database is used as input to the transformation process and consists of multiple operational databases. “Part V – Data Warehousing Granularity and Evolution” first introduces the concept of aggregation levels in a star schema constellation. It then focuses on the lowest-level star schema, including how to design a star schema and why it is needed in data warehousing, before moving on to methods for adding and removing dimensions. The remaining two chapters present more advanced concepts in data warehousing granularity and introduce the concept of active data warehousing. “Part VI – OLAP, Business Intelligence, and Data Analytics” thoroughly explains OLAP – online analytical processing, and describes two important activities in the data warehousing process, namely pre-data warehousing and post-data warehousing. The final chapter focuses on data analytics, which consists of a suite of methods for data analysis suitable for data warehousing. This textbook approaches data warehousing from the case study angle. Each chapter presents one or more case studies to thoroughly explain the concepts and has different levels of difficulty, hence learning is incremental. In addition, every chapter has also a section on further readings which give pointers and references to research papers related to the chapter. All these features make the book ideally suited for either introductory courses on data warehousing and data analytics, or even for self-studies by professionals. The book is accompanied by a web page that includes all the used datasets and codes as well as slides and solutions to exercises.



Data Warehousing And Analytics


Data Warehousing And Analytics
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Author : David Taniar
language : en
Publisher:
Release Date : 2021

Data Warehousing And Analytics written by David Taniar and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Data warehousing categories.


This textbook covers all central activities of data warehousing and analytics, including transformation, preparation, aggregation, integration, and analysis. It discusses the full spectrum of the journey of data from operational/transactional databases, to data warehouses and data analytics; as well as the role that data warehousing plays in the data processing lifecycle. It also explains in detail how data warehouses may be used by data engines, such as BI tools and analytics algorithms to produce reports, dashboards, patterns, and other useful information and knowledge. The book is divided into six parts, ranging from the basics of data warehouse design (Part I - Star Schema, Part II - Snowflake and Bridge Tables, Part III - Advanced Dimensions, and Part IV - Multi-Fact and Multi-Input), to more advanced data warehousing concepts (Part V - Data Warehousing and Evolution) and data analytics (Part VI - OLAP, BI, and Analytics). This textbook approaches data warehousing from the case study angle. Each chapter presents one or more case studies to thoroughly explain the concepts and has different levels of difficulty, hence learning is incremental. In addition, every chapter has also a section on further readings which give pointers and references to research papers related to the chapter. All these features make the book ideally suited for either introductory courses on data warehousing and data analytics, or even for self-studies by professionals. The book is accompanied by a web page that includes all the used datasets and codes as well as slides and solutions to exercises.



Progressive Methods In Data Warehousing And Business Intelligence Concepts And Competitive Analytics


Progressive Methods In Data Warehousing And Business Intelligence Concepts And Competitive Analytics
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Author : Taniar, David
language : en
Publisher: IGI Global
Release Date : 2009-02-28

Progressive Methods In Data Warehousing And Business Intelligence Concepts And Competitive Analytics written by Taniar, David and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-02-28 with Computers categories.


Provides developments and research, as well as current innovative activities in data warehousing and mining, focusing on the intersection of data warehousing and business intelligence.



Big Data


Big Data
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Author : Maribel Yasmina Santos
language : en
Publisher: CRC Press
Release Date : 2022-09-01

Big Data written by Maribel Yasmina Santos and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-01 with Computers categories.


Big Data is a concept of major relevance in today’s world, sometimes highlighted as a key asset for productivity growth, innovation, and customer relationship, whose popularity has increased considerably during the last years. Areas like smart cities, manufacturing, retail, finance, software development, environment, digital media, among others, can benefit from the collection, storage, processing, and analysis of Big Data, leveraging unprecedented data-driven workflows and considerably improved decision-making processes. The concept of a Big Data Warehouse (BDW) is emerging as either an augmentation or a replacement of the traditional Data Warehouse (DW), a concept that has a long history as one of the most valuable enterprise data assets. Nevertheless, research in Big Data Warehousing is still in its infancy, lacking an integrated and validated approach for designing and implementing both the logical layer (data models, data flows, and interoperability between components) and the physical layer (technological infrastructure) of these complex systems. This book addresses models and methods for designing and implementing Big Data Systems to support mixed and complex decision processes, giving special attention to BDWs as a way of efficiently storing and processing batch or streaming data for structured or semi-structured analytical problems.



Data Warehouse Systems


Data Warehouse Systems
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Author : Alejandro Vaisman
language : en
Publisher: Springer
Release Date : 2014-09-10

Data Warehouse Systems written by Alejandro Vaisman and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-09-10 with Computers categories.


With this textbook, Vaisman and Zimányi deliver excellent coverage of data warehousing and business intelligence technologies ranging from the most basic principles to recent findings and applications. To this end, their work is structured into three parts. Part I describes “Fundamental Concepts” including multi-dimensional models; conceptual and logical data warehouse design and MDX and SQL/OLAP. Subsequently, Part II details “Implementation and Deployment,” which includes physical data warehouse design; data extraction, transformation, and loading (ETL) and data analytics. Lastly, Part III covers “Advanced Topics” such as spatial data warehouses; trajectory data warehouses; semantic technologies in data warehouses and novel technologies like Map Reduce, column-store databases and in-memory databases. As a key characteristic of the book, most of the topics are presented and illustrated using application tools. Specifically, a case study based on the well-known Northwind database illustrates how the concepts presented in the book can be implemented using Microsoft Analysis Services and Pentaho Business Analytics. All chapters are summarized using review questions and exercises to support comprehensive student learning. Supplemental material to assist instructors using this book as a course text is available at http://cs.ulb.ac.be/DWSDIbook/, including electronic versions of the figures, solutions to all exercises, and a set of slides accompanying each chapter. Overall, students, practitioners and researchers alike will find this book the most comprehensive reference work on data warehouses, with key topics described in a clear and educational style.



Data Warehousing In The Age Of Big Data


Data Warehousing In The Age Of Big Data
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Author : Krish Krishnan
language : en
Publisher: Newnes
Release Date : 2013-05-02

Data Warehousing In The Age Of Big Data written by Krish Krishnan and has been published by Newnes this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-05-02 with Computers categories.


Data Warehousing in the Age of the Big Data will help you and your organization make the most of unstructured data with your existing data warehouse. As Big Data continues to revolutionize how we use data, it doesn't have to create more confusion. Expert author Krish Krishnan helps you make sense of how Big Data fits into the world of data warehousing in clear and concise detail. The book is presented in three distinct parts. Part 1 discusses Big Data, its technologies and use cases from early adopters. Part 2 addresses data warehousing, its shortcomings, and new architecture options, workloads, and integration techniques for Big Data and the data warehouse. Part 3 deals with data governance, data visualization, information life-cycle management, data scientists, and implementing a Big Data–ready data warehouse. Extensive appendixes include case studies from vendor implementations and a special segment on how we can build a healthcare information factory. Ultimately, this book will help you navigate through the complex layers of Big Data and data warehousing while providing you information on how to effectively think about using all these technologies and the architectures to design the next-generation data warehouse. Learn how to leverage Big Data by effectively integrating it into your data warehouse. Includes real-world examples and use cases that clearly demonstrate Hadoop, NoSQL, HBASE, Hive, and other Big Data technologies Understand how to optimize and tune your current data warehouse infrastructure and integrate newer infrastructure matching data processing workloads and requirements



The Modern Data Warehouse In Azure


The Modern Data Warehouse In Azure
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Author : Matt How
language : en
Publisher: Apress
Release Date : 2020-06-15

The Modern Data Warehouse In Azure written by Matt How and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-15 with Computers categories.


Build a modern data warehouse on Microsoft's Azure Platform that is flexible, adaptable, and fast—fast to snap together, reconfigure, and fast at delivering results to drive good decision making in your business. Gone are the days when data warehousing projects were lumbering dinosaur-style projects that took forever, drained budgets, and produced business intelligence (BI) just in time to tell you what to do 10 years ago. This book will show you how to assemble a data warehouse solution like a jigsaw puzzle by connecting specific Azure technologies that address your own needs and bring value to your business. You will see how to implement a range of architectural patterns using batches, events, and streams for both data lake technology and SQL databases. You will discover how to manage metadata and automation to accelerate the development of your warehouse while establishing resilience at every level. And you will know how to feed downstream analytic solutions such as Power BI and Azure Analysis Services to empower data-driven decision making that drives your business forward toward a pattern of success. This book teaches you how to employ the Azure platform in a strategy to dramatically improve implementation speed and flexibility of data warehousing systems. You will know how to make correct decisions in design, architecture, and infrastructure such as choosing which type of SQL engine (from at least three options) best meets the needs of your organization. You also will learn about ETL/ELT structure and the vast number of accelerators and patterns that can be used to aid implementation and ensure resilience. Data warehouse developers and architects will find this book a tremendous resource for moving their skills into the future through cloud-based implementations. What You Will LearnChoose the appropriate Azure SQL engine for implementing a given data warehouse Develop smart, reusable ETL/ELT processes that are resilient and easily maintained Automate mundane development tasks through tools such as PowerShell Ensure consistency of data by creating and enforcing data contracts Explore streaming and event-driven architectures for data ingestionCreate advanced staging layers using Azure Data Lake Gen 2 to feed your data warehouse Who This Book Is For Data warehouse or ETL/ELT developers who wish to implement a data warehouse project in the Azure cloud, and developers currently working in on-premise environments who want to move to the cloud, and for developers with Azure experience looking to tighten up their implementation and consolidate their knowledge



Oracle Data Warehousing And Business Intelligence Solutions


Oracle Data Warehousing And Business Intelligence Solutions
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Author : Robert Stackowiak
language : en
Publisher: John Wiley & Sons
Release Date : 2007-01-06

Oracle Data Warehousing And Business Intelligence Solutions written by Robert Stackowiak 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 2007-01-06 with Computers categories.


Up-to-date, comprehensive coverage of the Oracle database and business intelligence tools Written by a team of Oracle insiders, this authoritative book provides you with the most current coverage of the Oracle data warehousing platform as well as the full suite of business intelligence tools. You'll learn how to leverage Oracle features and how those features can be used to provide solutions to a variety of needs and demands. Plus, you'll get valuable tips and insight based on the authors' real-world experiences and their own implementations. Avoid many common pitfalls while learning best practices for: Leveraging Oracle technologies to design, build, and manage data warehouses Integrating specific database and business intelligence solutions from other vendors Using the new suite of Oracle business intelligence tools to analyze data for marketing, sales, and more Handling typical data warehouse performance challenges Uncovering initiatives by your business community, security business sponsorship, project staffing, and managing risk