Data Warehouse
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Building The Data Warehouse
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Author : W. H. Inmon
language : en
Publisher: John Wiley & Sons
Release Date : 2005-10-03
Building The Data Warehouse written by W. H. Inmon 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 2005-10-03 with Computers categories.
The new edition of the classic bestseller that launched thedata warehousing industry covers new approaches and technologies,many of which have been pioneered by Inmon himself In addition to explaining the fundamentals of data warehousesystems, the book covers new topics such as methods for handlingunstructured data in a data warehouse and storing data acrossmultiple storage media Discusses the pros and cons of relational versusmultidimensional design and how to measure return on investment inplanning data warehouse projects Covers advanced topics, including data monitoring andtesting Although the book includes an extra 100 pages worth of valuablecontent, the price has actually been reduced from $65 to $55
Learn Data Warehousing In 24 Hours
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Author : Alex Nordeen
language : en
Publisher: Guru99
Release Date : 2020-09-15
Learn Data Warehousing In 24 Hours written by Alex Nordeen and has been published by Guru99 this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-15 with Computers categories.
Unlike popular belief, Data Warehouse is not a single tool but a collection of software tools. A data warehouse will collect data from diverse sources into a single database. Using Business Intelligence tools, meaningful insights are drawn from this data. The best thing about “Learn Data Warehousing in 1 Day" is that it is small and can be completed in a day. With this e-book, you will be enough knowledge to contribute and participate in a Data warehouse implementation project. The book covers upcoming and promising technologies like Data Lakes, Data Mart, ELT (Extract Load Transform) amongst others. Following are detailed topics included in the book Table Of Content Chapter 1: What Is Data Warehouse? 1. What is Data Warehouse? 2. Types of Data Warehouse 3. Who needs Data warehouse? 4. Why We Need Data Warehouse? 5. Data Warehouse Tools Chapter 2: Data Warehouse Architecture 1. Characteristics of Data warehouse 2. Data Warehouse Architectures 3. Datawarehouse Components 4. Query Tools Chapter 3: ETL Process 1. What is ETL? 2. Why do you need ETL? 3. ETL Process 4. ETL tools Chapter 4: ETL Vs ELT 1. What is ETL? 2. Difference between ETL vs. ELT Chapter 5: Data Modeling 1. What is Data Modelling? 2. Types of Data Models 3. Characteristics of a physical data model Chapter 6: OLAP 1. What is Online Analytical Processing? 2. Types of OLAP systems 3. Advantages and Disadvantages of OLAP Chapter 7: Multidimensional Olap (MOLAP) 1. What is MOLAP? 2. MOLAP Architecture 3. MOLAP Tools Chapter 8: OLAP Vs OLTP 1. What is the meaning of OLAP? 2. What is the meaning of OLTP? 3. Difference between OLTP and OLAP Chapter 9: Dimensional Modeling 1. What is Dimensional Model? 2. Elements of Dimensional Data Model 3. Attributes 4. Difference between Dimension table vs. Fact table 5. Steps of Dimensional Modelling 6. Rules for Dimensional Modelling Chapter 10: Star and SnowFlake Schema 1. What is Multidimensional schemas? 2. What is a Star Schema? 3. What is a Snowflake Schema? 4. Difference between Start Schema and Snowflake Chapter 11: Data Mart 1. What is Data Mart? 2. Type of Data Mart 3. Steps in Implementing a Datamart Chapter 12: Data Mart Vs Data Warehouse 1. What is Data Warehouse? 2. What is Data Mart? 3. Differences between a Data Warehouse and a Data Mart Chapter 13: Data Lake 1. What is Data Lake? 2. Data Lake Architecture 3. Key Data Lake Concepts 4. Maturity stages of Data Lake Chapter 14: Data Lake Vs Data Warehouse 1. What is Data Warehouse? 2. What is Data Lake? 3. Key Difference between the Data Lake and Data Warehouse Chapter 15: What Is Business Intelligence? 1. What is Business Intelligence 2. Why is BI important? 3. How Business Intelligence systems are implemented? 4. Four types of BI users Chapter 16: Data Mining 1. What is Data Mining? 2. Types of Data 3. Data Mining Process 4. Modelling 5. Data Mining Techniques Chapter 17: Data Warehousing Vs Data Mining 1. What is Data warehouse? 2. What Is Data Mining? 3. Difference between Data mining and Data Warehousing?
Data Warehousing 101
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Author : Arshad Khan
language : en
Publisher: iUniverse
Release Date : 2003
Data Warehousing 101 written by Arshad Khan and has been published by iUniverse this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with Computers categories.
Data Warehousing 101: Concepts and Implementation will appeal to those planning data warehouse projects, senior executives, project managers, and project implementation team members. It will also be useful to functional managers, business analysts, developers, power users, and end-users. Data Warehousing 101: Concepts and Implementation, which can be used as a textbook in an introductory data warehouse course, can also be used as a supplemental text in IT courses that cover the subject of data warehousing. Data Warehousing 101: Concepts and Implementation reviews the evolution of data warehousing and its growth drivers, process and architecture, data warehouse characteristics and design, data marts, multi-dimensionality, and OLAP. It also shows how to plan a data warehouse project as well as build and operate data warehouses. Data Warehousing 101: Concepts and Implementation also covers, in depth, common failure causes and mistakes and provides useful guidelines and tips for avoiding common mistakes.
Data Warehouse
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Author : Niken Hendrakusma Wardani
language : id
Publisher: Universitas Brawijaya Press
Release Date : 2019-11-30
Data Warehouse written by Niken Hendrakusma Wardani and has been published by Universitas Brawijaya Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-30 with Computers categories.
Buku Ajar Data Warehouse memuat 5 bab yang telah disesuikan dengan topik-topik utama pada pokok bahasan pada Rencana Pembelajaran Semester (RPS) Mata Kuliah Data Warehouse (MK DW). Pada setiap bab ada tujuan instruksional, paparan materi, studi kasus dan latihan soal (umpan balik). Buku ini dibuat sesuai kebutuhan belajar mahasiswa selama satu semester yang terdiri dari konsep dan praktik mandiri. Bab 1 menjelaskan ringkasan konsep-konsep data warehouse yaitu definisi DW, karakteristik DW, arsitektur DW dan fase-fase pengembangan DW. Bab 2 menjelaskan tahapan melakukan analisis kebutuhan data warehouse yang harus memperhatikan konsep granularity, fakta, dimensi, metode pengumpulan data dan information package. Bab 3 menjelaskan desain data warehouse yang disertai contoh agar mahasiswa bisa praktik mengimplementasikan desain DW berdasarkan studi kasus yang diberikan. Bab 4 menjelaskan konsep ETL dan praktik membuat job ETL menggunakan software open source Talend Open Studio. Bab 5 menjelaskan konsep SQL OLAP yang dapat digunakan untuk penyajian data dari DW. Konsep-konsep yang dijabarkan pada buku ini diambil dari berbagai referensi kredibel dan pakar-pakar DW seperti W.H. Inmon dan Ralph Kimball. Sedangkan studi kasus diambil dari pengalaman tim dosen selama mengajar MK DW agar disesuaikan dengan kemampuan mahasiswa dalam mempraktikkan implementasi DW terhadap masalah yang diberikan. Buku ini juga diharapkan dapat menjembatani mahasiswa dalam memahami buku-buku pustaka berbahasa asing terkait topik-topik DW. Pada beberapa konsep juga ditambahkan literatur terkini dari hasil penelitian terkait topik DW seperti istilah tradisional DW dan real-time DW yang jarang dibahas pada textbook.
Building A Scalable Data Warehouse With Data Vault 2 0
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Author : Dan Linstedt
language : en
Publisher: Morgan Kaufmann
Release Date : 2015-09-15
Building A Scalable Data Warehouse With Data Vault 2 0 written by Dan Linstedt and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-09-15 with Computers categories.
The Data Vault was invented by Dan Linstedt at the U.S. Department of Defense, and the standard has been successfully applied to data warehousing projects at organizations of different sizes, from small to large-size corporations. Due to its simplified design, which is adapted from nature, the Data Vault 2.0 standard helps prevent typical data warehousing failures. "Building a Scalable Data Warehouse" covers everything one needs to know to create a scalable data warehouse end to end, including a presentation of the Data Vault modeling technique, which provides the foundations to create a technical data warehouse layer. The book discusses how to build the data warehouse incrementally using the agile Data Vault 2.0 methodology. In addition, readers will learn how to create the input layer (the stage layer) and the presentation layer (data mart) of the Data Vault 2.0 architecture including implementation best practices. Drawing upon years of practical experience and using numerous examples and an easy to understand framework, Dan Linstedt and Michael Olschimke discuss: How to load each layer using SQL Server Integration Services (SSIS), including automation of the Data Vault loading processes. Important data warehouse technologies and practices. Data Quality Services (DQS) and Master Data Services (MDS) in the context of the Data Vault architecture. Provides a complete introduction to data warehousing, applications, and the business context so readers can get-up and running fast Explains theoretical concepts and provides hands-on instruction on how to build and implement a data warehouse Demystifies data vault modeling with beginning, intermediate, and advanced techniques Discusses the advantages of the data vault approach over other techniques, also including the latest updates to Data Vault 2.0 and multiple improvements to Data Vault 1.0
Building The Data Warehouse
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Author : William H. Inmon
language : en
Publisher: QED Information Sciences
Release Date : 1992
Building The Data Warehouse written by William H. Inmon and has been published by QED Information Sciences this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with Business & Economics categories.
"Data warehouses provide a much-needed strategy for organizations to collect, store, and analyze vast amounts of business data. As businesses expand both brick-and-mortar and online activities, the field of data warehousing has become increasingly important. Since it was first published in 1990, W. H. Inmon's Building the Data Warehouse has been the bible of data warehousing - it is the book that launched the data warehousing industry and it remains the preeminent introduction to the subject. This new edition covers the latest developments with this technology, many of which have been pioneered by Inmon himself."--BOOK JACKET.
Data Warehouse Systems
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Author : Alejandro Vaisman
language : en
Publisher: Springer Nature
Release Date : 2022-08-16
Data Warehouse Systems written by Alejandro Vaisman and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-16 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 conceptual and logical data warehouse design, as well as querying using MDX, DAX and SQL/OLAP. This part also covers data analytics using Power BI and Analysis Services. Part II details “Implementation and Deployment,” including physical design, ETL and data warehouse design methodologies. Part III covers “Advanced Topics” and it is almost completely new in this second edition. This part includes chapters with an in-depth coverage of temporal, spatial, and mobility data warehousing. Graph data warehouses are also covered in detail using Neo4j. The last chapter extensively studies big data management and the usage of Hadoop, Spark, distributed, in-memory, columnar, NoSQL and NewSQL database systems, and data lakes in the context of analytical data processing. 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 Power BI. All chapters have been revised and updated to the latest versions of the software tools used. KPIs and Dashboards are now also developed using DAX and Power BI, and the chapter on ETL has been expanded with the implementation of ETL processes in PostgreSQL. Review questions and exercises complement each chapter to support comprehensive student learning. Supplemental material to assist instructors using this book as a course text is available online and includes 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. “I can only invite you to dive into the contents of the book, feeling certain that once you have completed its reading (or maybe, targeted parts of it), you will join me in expressing our gratitude to Alejandro and Esteban, for providing such a comprehensive textbook for the field of data warehousing in the first place, and for keeping it up to date with the recent developments, in this current second edition.” From the foreword by Panos Vassiliadis, University of Ioannina, Greece.
The Data Warehouse Lifecycle Toolkit
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Author : Ralph Kimball
language : en
Publisher: John Wiley & Sons
Release Date : 2011-03-08
The Data Warehouse Lifecycle Toolkit written by Ralph Kimball 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 2011-03-08 with Computers categories.
A thorough update to the industry standard for designing, developing, and deploying data warehouse and business intelligence systems The world of data warehousing has changed remarkably since the first edition of The Data Warehouse Lifecycle Toolkit was published in 1998. In that time, the data warehouse industry has reached full maturity and acceptance, hardware and software have made staggering advances, and the techniques promoted in the premiere edition of this book have been adopted by nearly all data warehouse vendors and practitioners. In addition, the term "business intelligence" emerged to reflect the mission of the data warehouse: wrangling the data out of source systems, cleaning it, and delivering it to add value to the business. Ralph Kimball and his colleagues have refined the original set of Lifecycle methods and techniques based on their consulting and training experience. The authors understand first-hand that a data warehousing/business intelligence (DW/BI) system needs to change as fast as its surrounding organization evolves. To that end, they walk you through the detailed steps of designing, developing, and deploying a DW/BI system. You'll learn to create adaptable systems that deliver data and analyses to business users so they can make better business decisions.
Data Warehousing For Dummies
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Author : Thomas C. Hammergren
language : en
Publisher: John Wiley & Sons
Release Date : 2009-04-13
Data Warehousing For Dummies written by Thomas C. Hammergren 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 2009-04-13 with Computers categories.
Data warehousing is one of the hottest business topics, and there’s more to understanding data warehousing technologies than you might think. Find out the basics of data warehousing and how it facilitates data mining and business intelligence with Data Warehousing For Dummies, 2nd Edition. Data is probably your company’s most important asset, so your data warehouse should serve your needs. The fully updated Second Edition of Data Warehousing For Dummies helps you understand, develop, implement, and use data warehouses, and offers a sneak peek into their future. You’ll learn to: Analyze top-down and bottom-up data warehouse designs Understand the structure and technologies of data warehouses, operational data stores, and data marts Choose your project team and apply best development practices to your data warehousing projects Implement a data warehouse, step by step, and involve end-users in the process Review and upgrade existing data storage to make it serve your needs Comprehend OLAP, column-wise databases, hardware assisted databases, and middleware Use data mining intelligently and find what you need Make informed choices about consultants and data warehousing products Data Warehousing For Dummies, 2nd Edition also shows you how to involve users in the testing process and gain valuable feedback, what it takes to successfully manage a data warehouse project, and how to tell if your project is on track. You’ll find it’s the most useful source of data on the topic!
The Data Warehouse Etl Toolkit
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Author : Ralph Kimball
language : en
Publisher: John Wiley & Sons
Release Date : 2011-04-27
The Data Warehouse Etl Toolkit written by Ralph Kimball 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 2011-04-27 with Computers categories.
Cowritten by Ralph Kimball, the world's leading data warehousing authority, whose previous books have sold more than 150,000 copies Delivers real-world solutions for the most time- and labor-intensive portion of data warehousing-data staging, or the extract, transform, load (ETL) process Delineates best practices for extracting data from scattered sources, removing redundant and inaccurate data, transforming the remaining data into correctly formatted data structures, and then loading the end product into the data warehouse Offers proven time-saving ETL techniques, comprehensive guidance on building dimensional structures, and crucial advice on ensuring data quality