Advertisement
| financial data warehouse model: The Data Warehouse Toolkit Ralph Kimball, Margy Ross, 2011-08-08 This old edition was published in 2002. The current and final edition of this book is The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd Edition which was published in 2013 under ISBN: 9781118530801. The authors begin with fundamental design recommendations and gradually progress step-by-step through increasingly complex scenarios. Clear-cut guidelines for designing dimensional models are illustrated using real-world data warehouse case studies drawn from a variety of business application areas and industries, including: Retail sales and e-commerce Inventory management Procurement Order management Customer relationship management (CRM) Human resources management Accounting Financial services Telecommunications and utilities Education Transportation Health care and insurance By the end of the book, you will have mastered the full range of powerful techniques for designing dimensional databases that are easy to understand and provide fast query response. You will also learn how to create an architected framework that integrates the distributed data warehouse using standardized dimensions and facts. |
| financial data warehouse model: Financial Data Engineering Tamer Khraisha, 2024-10-09 Today, investment in financial technology and digital transformation is reshaping the financial landscape and generating many opportunities. Too often, however, engineers and professionals in financial institutions lack a practical and comprehensive understanding of the concepts, problems, techniques, and technologies necessary to build a modern, reliable, and scalable financial data infrastructure. This is where financial data engineering is needed. A data engineer developing a data infrastructure for a financial product possesses not only technical data engineering skills but also a solid understanding of financial domain-specific challenges, methodologies, data ecosystems, providers, formats, technological constraints, identifiers, entities, standards, regulatory requirements, and governance. This book offers a comprehensive, practical, domain-driven approach to financial data engineering, featuring real-world use cases, industry practices, and hands-on projects. You'll learn: The data engineering landscape in the financial sector Specific problems encountered in financial data engineering The structure, players, and particularities of the financial data domain Approaches to designing financial data identification and entity systems Financial data governance frameworks, concepts, and best practices The financial data engineering lifecycle from ingestion to production The varieties and main characteristics of financial data workflows How to build financial data pipelines using open source tools and APIs Tamer Khraisha, PhD, is a senior data engineer and scientific author with more than a decade of experience in the financial sector. |
| financial data warehouse model: Building a Data Warehouse Vincent Rainardi, 2008-03-11 Here is the ideal field guide for data warehousing implementation. This book first teaches you how to build a data warehouse, including defining the architecture, understanding the methodology, gathering the requirements, designing the data models, and creating the databases. Coverage then explains how to populate the data warehouse and explores how to present data to users using reports and multidimensional databases and how to use the data in the data warehouse for business intelligence, customer relationship management, and other purposes. It also details testing and how to administer data warehouse operation. |
| financial data warehouse model: Data Warehousing Mark Humphries, Michael W. Hawkins, Michelle C. Dy, 1999 PLEASE PROVIDE COURSE INFORMATION PLEASE PROVIDE |
| financial data warehouse model: Corporate Information Factory W. H. Inmon, Claudia Imhoff, Ryan Sousa, 2002-03-14 The father of data warehousing incorporates the latesttechnologies into his blueprint for integrated decision supportsystems Today's corporate IT and data warehouse managers are required tomake a small army of technologies work together to ensure fast andaccurate information for business managers. Bill Inmon created theCorporate Information Factory to solve the needs ofthese managers. Since the First Edition, the design of the factoryhas grown and changed dramatically. This Second Edition, revisedand expanded by 40% with five new chapters, incorporates thesechanges. This step-by-step guide will enable readers to connecttheir legacy systems with the data warehouse and deal with a hostof new and changing technologies, including Web access mechanisms,e-commerce systems, ERP (Enterprise Resource Planning) systems. Thebook also looks closely at exploration and data mining servers foranalyzing customer behavior and departmental data marts forfinance, sales, and marketing. |
| financial data warehouse model: The Data Model Resource Book, Volume 1 Len Silverston, 2011-08-08 A quick and reliable way to build proven databases for core business functions Industry experts raved about The Data Model Resource Book when it was first published in March 1997 because it provided a simple, cost-effective way to design databases for core business functions. Len Silverston has now revised and updated the hugely successful 1st Edition, while adding a companion volume to take care of more specific requirements of different businesses. This updated volume provides a common set of data models for specific core functions shared by most businesses like human resources management, accounting, and project management. These models are standardized and are easily replicated by developers looking for ways to make corporate database development more efficient and cost effective. This guide is the perfect complement to The Data Model Resource CD-ROM, which is sold separately and provides the powerful design templates discussed in the book in a ready-to-use electronic format. A free demonstration CD-ROM is available with each copy of the print book to allow you to try before you buy the full CD-ROM. |
| financial data warehouse model: Building the Data Warehouse W. H. Inmon, 2005-10-03 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 |
| financial data warehouse model: Designing a Data Warehouse Chris Todman, 2001 PLEASE PROVIDE COURSE INFORMATION PLEASE PROVIDE |
| financial data warehouse model: The Data Warehouse Toolkit Ralph Kimball, Margy Ross, 2013-07-01 Updated new edition of Ralph Kimball's groundbreaking book on dimensional modeling for data warehousing and business intelligence! The first edition of Ralph Kimball's The Data Warehouse Toolkit introduced the industry to dimensional modeling, and now his books are considered the most authoritative guides in this space. This new third edition is a complete library of updated dimensional modeling techniques, the most comprehensive collection ever. It covers new and enhanced star schema dimensional modeling patterns, adds two new chapters on ETL techniques, includes new and expanded business matrices for 12 case studies, and more. Authored by Ralph Kimball and Margy Ross, known worldwide as educators, consultants, and influential thought leaders in data warehousing and business intelligence Begins with fundamental design recommendations and progresses through increasingly complex scenarios Presents unique modeling techniques for business applications such as inventory management, procurement, invoicing, accounting, customer relationship management, big data analytics, and more Draws real-world case studies from a variety of industries, including retail sales, financial services, telecommunications, education, health care, insurance, e-commerce, and more Design dimensional databases that are easy to understand and provide fast query response with The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd Edition. |
| financial data warehouse model: Agile Data Warehouse Design Lawrence Corr, Jim Stagnitto, 2011-11 Agile Data Warehouse Design is a step-by-step guide for capturing data warehousing/business intelligence (DW/BI) requirements and turning them into high performance dimensional models in the most direct way: by modelstorming (data modeling + brainstorming) with BI stakeholders. This book describes BEAM✲, an agile approach to dimensional modeling, for improving communication between data warehouse designers, BI stakeholders and the whole DW/BI development team. BEAM✲ provides tools and techniques that will encourage DW/BI designers and developers to move away from their keyboards and entity relationship based tools and model interactively with their colleagues. The result is everyone thinks dimensionally from the outset! Developers understand how to efficiently implement dimensional modeling solutions. Business stakeholders feel ownership of the data warehouse they have created, and can already imagine how they will use it to answer their business questions. Within this book, you will learn: ✲ Agile dimensional modeling using Business Event Analysis & Modeling (BEAM✲) ✲ Modelstorming: data modeling that is quicker, more inclusive, more productive, and frankly more fun! ✲ Telling dimensional data stories using the 7Ws (who, what, when, where, how many, why and how) ✲ Modeling by example not abstraction; using data story themes, not crow's feet, to describe detail ✲ Storyboarding the data warehouse to discover conformed dimensions and plan iterative development ✲ Visual modeling: sketching timelines, charts and grids to model complex process measurement - simply ✲ Agile design documentation: enhancing star schemas with BEAM✲ dimensional shorthand notation ✲ Solving difficult DW/BI performance and usability problems with proven dimensional design patterns Lawrence Corr is a data warehouse designer and educator. As Principal of DecisionOne Consulting, he helps clients to review and simplify their data warehouse designs, and advises vendors on visual data modeling techniques. He regularly teaches agile dimensional modeling courses worldwide and has taught dimensional DW/BI skills to thousands of students. Jim Stagnitto is a data warehouse and master data management architect specializing in the healthcare, financial services, and information service industries. He is the founder of the data warehousing and data mining consulting firm Llumino. |
| financial data warehouse model: Building the Data Warehouse W. H. Inmon, 2002-10-01 The data warehousing bible updated for the new millennium Updated and expanded to reflect the many technological advances occurring since the previous edition, this latest edition of the data warehousing bible provides a comprehensive introduction to building data marts, operational data stores, the Corporate Information Factory, exploration warehouses, and Web-enabled warehouses. Written by the father of the data warehouse concept, the book also reviews the unique requirements for supporting e-business and explores various ways in which the traditional data warehouse can be integrated with new technologies to provide enhanced customer service, sales, and support-both online and offline-including near-line data storage techniques. |
| financial data warehouse model: Strategic Information Systems: Concepts, Methodologies, Tools, and Applications Hunter, M. Gordon, 2009-08-31 This 4-volume set provides a compendium of comprehensive advanced research articles written by an international collaboration of experts involved with the strategic use of information systems--Provided by publisher. |
| financial data warehouse model: Data Warehouse Design Solutions Christopher Adamson, Michael Venerable, 1998-07-13 Each chapter is... a practice run for the way we all ought to design our data marts and hence our data warehouses.-Ralph Kimball, from the Foreword. Let the experts show you how to customize data warehouse designs for real business needs in Data Warehouse Design Solutions. To effectively design a data warehouse, you have to understand its many business uses. This guidebook shows you how business managers in different corporate functions actually use data warehouses to make decisions. You'll get a rich set of data warehouse designs that flow from realistic business cases. Two top experts show you how to customize your data warehouse designs for real-life business needs including: * Sales and marketing * Production and inventory management * Budgeting and financial reporting * Quality control * Product delivery and fulfillment * Strategic business analysis such as determining market share, rates of return on investment, and other key analytic ratios. CD-ROM includes All sample data warehouse designs with accompanying preformatted reports in HTML for specific business uses such as marketing, sales, and financial analysis. |
| financial data warehouse model: Data Warehousing Fundamentals Paulraj Ponniah, 2004-04-07 Geared to IT professionals eager to get into the all-importantfield of data warehousing, this book explores all topics needed bythose who design and implement data warehouses. Readers will learnabout planning requirements, architecture, infrastructure, datapreparation, information delivery, implementation, and maintenance.They'll also find a wealth of industry examples garnered from theauthor's 25 years of experience in designing and implementingdatabases and data warehouse applications for majorcorporations. Market: IT Professionals, Consultants. |
| financial data warehouse model: Oracle Data Warehousing and Business Intelligence Solutions Robert Stackowiak, Joseph Rayman, Rick Greenwald, 2007-01-06 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 |
| financial data warehouse model: Mastering Data Warehousing Cybellium Ltd, Architect, Build, and Optimize Your Data Warehouse Are you ready to revolutionize the way your organization stores and accesses data? Mastering Data Warehousing is your definitive guide to architecting, building, and optimizing data warehouses that facilitate efficient data storage and retrieval. Whether you're a data architect designing robust warehouse structures or a business leader aiming to glean insights from your data, this book equips you with the knowledge and strategies to master the art of data warehousing. Key Features: 1. Architecting Data Warehouses: Immerse yourself in the world of data warehousing, understanding its significance, challenges, and opportunities. Build a strong foundation that empowers you to design data warehouses that cater to your organization's needs. 2. Data Warehouse Models: Master various data warehouse models. Learn about star schema, snowflake schema, and other dimensional modeling techniques for organizing data for efficient querying and analysis. 3. Data ETL (Extract, Transform, Load): Uncover the power of ETL processes in data warehousing. Explore techniques for extracting data from diverse sources, transforming it for analysis, and loading it into your warehouse. 4. Data Quality and Governance: Delve into data quality and governance within data warehousing. Learn how to ensure data accuracy, consistency, and compliance within your warehouse. 5. Optimizing Query Performance: Master techniques for optimizing query performance. Learn about indexing, partitioning, and materialized views to enhance query speed and responsiveness. 6. Scalability and High Availability: Explore strategies for scaling and ensuring high availability of your data warehouse. Learn how to handle growing data volumes and ensure uninterrupted access to critical information. 7. Cloud Data Warehousing: Discover the world of cloud data warehousing. Learn about designing and migrating data warehouses to cloud platforms, enabling scalability and cost-efficiency. 8. Data Warehousing Tools and Platforms: Uncover a range of tools and platforms for data warehousing. Explore traditional solutions as well as modern technologies like columnar databases and data lakes. 9. Real-Time Data Warehousing: Dive into real-time data warehousing techniques. Learn how to capture and process streaming data for instant insights and decision-making. 10. Real-World Applications: Gain insights into real-world use cases of data warehousing across industries. From business intelligence to customer analytics, discover how organizations leverage data warehouses for strategic advantage. Who This Book Is For: Mastering Data Warehousing is an essential resource for data architects, analysts, and business professionals aiming to excel in designing and managing data warehouses. Whether you're enhancing your technical skills or transforming data into actionable insights, this book will guide you through the intricacies and empower you to harness the full potential of data warehousing. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com |
| financial data warehouse model: Handbook of Financial Data and Risk Information I Margarita S. Brose, Mark D. Flood, Dilip Krishna, Bill Nichols, 2014 Volume I examines the business and regulatory context that makes risk information so important. A vast set of quantitative techniques, internal risk measurement and governance processes, and supervisory reporting rules have grown up over time, all with important implications for modeling and managing risk information. Without an understanding of the broader forces at work, it is all too easy to get lost in the details. -- Back cover. |
| financial data warehouse model: E-Data Jill Dyché, 2000 Dyche presents the complete manager's briefing on what data warehousing technology can do today and how to achieve optimal results. Using real-world case studies from Charles Schwab, Bank of America, Qantas, 20th Century Fox, and others, she covers decision support, database marketing, and many industry-specific data warehouse applications. |
| financial data warehouse model: Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications Wang, John, 2008-05-31 In recent years, the science of managing and analyzing large datasets has emerged as a critical area of research. In the race to answer vital questions and make knowledgeable decisions, impressive amounts of data are now being generated at a rapid pace, increasing the opportunities and challenges associated with the ability to effectively analyze this data. |
| financial data warehouse model: Open Source Data Warehousing and Business Intelligence Lakshman Bulusu, 2012-08-06 Open Source Data Warehousing and Business Intelligence is an all-in-one reference for developing open source based data warehousing (DW) and business intelligence (BI) solutions that are business-centric, cross-customer viable, cross-functional, cross-technology based, and enterprise-wide. Considering the entire lifecycle of an open source DW & |
| financial data warehouse model: Rise of the Data Cloud Frank Slootman, Steve Hamm, 2020-12-18 The rise of the Data Cloud is ushering in a new era of computing. The world’s digital data is mass migrating to the cloud, where it can be more effectively integrated, managed, and mobilized. The data cloud eliminates data siloes and enables data sharing with business partners, capitalizing on data network effects. It democratizes data analytics, making the most sophisticated data science tools accessible to organizations of all sizes. Data exchanges enable businesses to discover, explore, and easily purchase or sell data—opening up new revenue streams. Business leaders have long dreamed of data driving their organizations. Now, thanks to the Data Cloud, nothing stands in their way. |
| financial data warehouse model: The Unified Star Schema Bill Inmon, Francesco Puppini, 2020-10 Master the most agile and resilient design for building analytics applications: the Unified Star Schema (USS) approach. The USS has many benefits over traditional dimensional modeling. Witness the power of the USS as a single star schema that serves as a foundation for all present and future business requirements of your organization. |
| financial data warehouse model: Oracle Data Warehouse Tuning for 10g Gavin JT Powell, 2011-04-08 This book should satisfy those who want a different perspective than the official Oracle documentation. It will cover all important aspects of a data warehouse while giving the necessary examples to make the reading a lively experience. - Tim Donar, Author and Systems Architect for Enterprise Data WarehousesTuning a data warehouse database focuses on large transactions, mostly requiring what is known as throughput. Throughput is the passing of large amounts of information through a server, network and Internet environment, backwards and forwards, constantly! The ultimate objective of a data warehouse is the production of meaningful and useful reporting, from historical and archived data. The trick is to make the reports print within an acceptable time frame.A data model contains tables and relationships between tables. Tuning a data model involves Normalization and Denormalization. Different approaches are required depending on the application, such as OLTP or a Data Warehouse. Inappropriate database design can make SQL code impossible to tune. Poor data modeling can have a most profound effect on database performance since all SQL code is constructed from the data model.* Takes users beyond basics to critical issues in running most efficient data warehouse applications* Illustrates how to keep data going in and out in the most productive way possible* Focus is placed on Data Warehouse performance tuning |
| financial data warehouse model: Beginning Database Design Clare Churcher, 2012-08-08 Beginning Database Design, Second Edition provides short, easy-to-read explanations of how to get database design right the first time. This book offers numerous examples to help you avoid the many pitfalls that entrap new and not-so-new database designers. Through the help of use cases and class diagrams modeled in the UML, you’ll learn to discover and represent the details and scope of any design problem you choose to attack. Database design is not an exact science. Many are surprised to find that problems with their databases are caused by poor design rather than by difficulties in using the database management software. Beginning Database Design, Second Edition helps you ask and answer important questions about your data so you can understand the problem you are trying to solve and create a pragmatic design capturing the essentials while leaving the door open for refinements and extension at a later stage. Solid database design principles and examples help demonstrate the consequences of simplifications and pragmatic decisions. The rationale is to try to keep a design simple, but allow room for development as situations change or resources permit. Provides solid design principles by which to avoid pitfalls and support changing needs Includes numerous examples of good and bad design decisions and their consequences Shows a modern method for documenting design using the Unified Modeling Language |
| financial data warehouse model: Technical Reference Model United States. Patent and Trademark Office, 1997 |
| financial data warehouse model: Building and Maintaining a Data Warehouse Fon Silvers, 2008-03-18 As it is with building a house, most of the work necessary to build a data warehouse is neither visible nor obvious when looking at the completed product. While it may be easy to plan for a data warehouse that incorporates all the right concepts, taking the steps needed to create a warehouse that is as functional and user-friendly as it is theoreti |
| financial data warehouse model: CFO Insights C. Cristian Wulf, 2006-07-28 The benefits Carrefour achieved have been substantially in excess of predictions. The Shared Service accounting centers enabled streamlined processes, lowered costs, and introduced standard processes, a standard system, and standard data for a global company. The new infrastructure can support rapid expansion and can add new stores with the flip of a switch. From a systems point of view, Carrefour now has a 'factory' in place to deliver high-efficiency systems, tools, processes, and training. --From Chapter 9, Implementation and Operational Imperatives for ERP The benefits of efficient information delivery are demonstrated by the results of one of the world's largest mySAP.com implementations. Siemens achieved a twenty-five percent cost reduction through streamlined information delivery and improved access to financial information. It also enhanced its reporting capabilities from seventy percent to nearly 100 percent through increased intranet availability. --From Chapter 3, Financial and Management Reporting Research shows that high-performance businesses and governments use finance technology as one of the capabilities to help executives make better decisions for resource allocation, while at the same time increasing productivity. CFO Insights: Enabling High Performance through Leading Practices for Finance ERP includes a number of case studies and lessons learned from Accenture clients across a variety of industries that have implemented, upgraded, and operated Oracle/PeopleSoft and SAP. Each case study highlights vital thoughts, benefits, and considerations and provides relevant guidance as one proceeds with an ERP on the journey toward high performance. |
| financial data warehouse model: Testing the Data Warehouse Practicum Wayne Yaddow Doug Vucevic &, 2012-08 The quality of a data warehouse (DWH) is the elusive aspect of it, not because it is hard to achieve [once we agree what it is], but because it is difficult to describe. We propose the notion that quality is not an attribute or a feature that a product has to possess, but rather a relationship between that product and each and every stakeholder. More specifically, the relationship between the software quality and the organization that produces the products is explored. Quality of data that populates the DWH is the main concern of the book, therefore we propose a definition for data quality as: fitness to serve each and every purpose. Methods are proposed throughout the book to help readers achieve data warehouse quality. |
| financial data warehouse model: Building a Scalable Data Warehouse with Data Vault 2.0 Daniel Linstedt, Michael Olschimke, 2015-09-15 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 |
| financial data warehouse model: Corporate Controller's Handbook of Financial Management 2008-2009 Jae K. Shim, Joel G. Siegel, Nick Dauber, 2008 CCH's Corporate Controller's Handbook of Financial Management is a comprehensive source of practical solutions, strategies, techniques, procedures, and formulas covering all key aspects of accounting and financial management. Its examples, checklists, step-by-step instructions, and other practical working tools simplify complex financial management issues and give CFOs, corporate financial managers, and controllers quick answers to day-to-day questions. |
| financial data warehouse model: The Modern Data Warehouse in Azure Matt How, 2020-06-15 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 |
| financial data warehouse model: Handbook of Financial Data and Risk Information II Margarita S. Brose, Mark D. Flood, Dilip Krishna, Bill Nichols, 2014-01-09 A comprehensive resource for understanding the issues involved in collecting, measuring and managing data in the financial services industry. |
| financial data warehouse model: The Data Warehouse Lifecycle Toolkit Ralph Kimball, Margy Ross, Warren Thornthwaite, Joy Mundy, Bob Becker, 2008-01-10 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. |
| financial data warehouse model: Data Mining and Data Warehousing Parteek Bhatia, 2019-06-27 Written in lucid language, this valuable textbook brings together fundamental concepts of data mining and data warehousing in a single volume. Important topics including information theory, decision tree, Naïve Bayes classifier, distance metrics, partitioning clustering, associate mining, data marts and operational data store are discussed comprehensively. The textbook is written to cater to the needs of undergraduate students of computer science, engineering and information technology for a course on data mining and data warehousing. The text simplifies the understanding of the concepts through exercises and practical examples. Chapters such as classification, associate mining and cluster analysis are discussed in detail with their practical implementation using Weka and R language data mining tools. Advanced topics including big data analytics, relational data models and NoSQL are discussed in detail. Pedagogical features including unsolved problems and multiple-choice questions are interspersed throughout the book for better understanding. |
| financial data warehouse model: The Data Model Resource Book, Volume 2 Len Silverston, 2001-03-21 A quick and reliable way to build proven databases for core business functions Industry experts raved about The Data Model Resource Book when it was first published in March 1997 because it provided a simple, cost-effective way to design databases for core business functions. Len Silverston has now revised and updated the hugely successful First Edition, while adding a companion volume to take care of more specific requirements of different businesses. Each volume is accompanied by a CD-ROM, which is sold separately. Each CD-ROM provides powerful design templates discussed in the books in a ready-to-use electronic format, allowing companies and individuals to develop the databases they need at a fraction of the cost and a third of the time it would take to build them from scratch. With each business function boasting its own directory, this CD-ROM provides a variety of data models for specific implementations in such areas as financial services, insurance, retail, healthcare, universities, and telecom. |
| financial data warehouse model: The Business of Data Vault Modeling Daniel Lindstedt, Kent Graziano, Hans Hultgren, 2009 |
| financial data warehouse model: The Data Model Resource Book Len Silverston, Paul Agnew, 2011-03-21 This third volume of the best-selling Data Model Resource Book series revolutionizes the data modeling discipline by answering the question How can you save significant time while improving the quality of any type of data modeling effort? In contrast to the first two volumes, this new volume focuses on the fundamental, underlying patterns that affect over 50 percent of most data modeling efforts. These patterns can be used to considerably reduce modeling time and cost, to jump-start data modeling efforts, as standards and guidelines to increase data model consistency and quality, and as an objective source against which an enterprise can evaluate data models. |
| financial data warehouse model: Databases Illuminated Catherine Ricardo, 2011-03-03 Integrates database theory with a practical approach to database design and implementation. From publisher description. |
| financial data warehouse model: Introduction to Data Mining and Its Applications S. Sumathi, S.N. Sivanandam, 2006-09-26 This book explores the concepts of data mining and data warehousing, a promising and flourishing frontier in data base systems and new data base applications and is also designed to give a broad, yet in-depth overview of the field of data mining. Data mining is a multidisciplinary field, drawing work from areas including database technology, AI, machine learning, NN, statistics, pattern recognition, knowledge based systems, knowledge acquisition, information retrieval, high performance computing and data visualization. This book is intended for a wide audience of readers who are not necessarily experts in data warehousing and data mining, but are interested in receiving a general introduction to these areas and their many practical applications. Since data mining technology has become a hot topic not only among academic students but also for decision makers, it provides valuable hidden business and scientific intelligence from a large amount of historical data. It is also written for technical managers and executives as well as for technologists interested in learning about data mining. |
| financial data warehouse model: Complete Analytics with IBM DB2 Query Management Facility: Accelerating Well-Informed Decisions Across the Enterprise Kristi Ramey, Mike Biere, Peter Richardson, Shawn Sullivan, Jeremy Weatherall, IBM Redbooks, 2012-08-20 There is enormous pressure today for businesses across all industries to cut costs, enhance business performance, and deliver greater value with fewer resources. To take business analytics to the next level and drive tangible improvements to the bottom line, it is important to manage not only the volume of data, but the speed with which actionable findings can be drawn from a wide variety of disparate sources. The findings must be easily communicated to those responsible for making both strategic and tactical decisions. At the same time, strained IT budgets require that the solution be self-service for everyone from DBAs to business users, and easily deployed to thin, browser-based clients. Business analytics hosted in the Query Management FacilityTM (QMFTM) on DB2® and System z® allow you to tackle these challenges in a practical way, using new features and functions that are easily deployed across the enterprise and easily consumed by business users who do not have prior IT experience. This IBM® Redbooks® publication provides step-by-step instructions on using these new features: Access to data that resides in any JDBC-compliant data source OLAP access through XMLA 150+ new analytical functions Graphical query interfaces and graphical reports Graphical, interactive dashboards Ability to integrate QMF functions with third-party applications Support for the IBM DB2 Analytics Accelerator A new QMF Classic perspective in QMF for Workstation Ability to start QMF for TSO as a DB2 for z/OS stored procedure New metadata capabilities, including ER diagrams and capability to federate data into a single virtual source |
Oracle Financial Services Data Foundation
With a fully physicalized data model, the Oracle Financial Services Data Foundation leverages 3,000 entities and 20,000 elements, which have been modeled for known use cases such as …
IBM Industry Models for Banking
IBM Banking Data Warehouse (BDW) comprises interconnected models and supporting tooling that accelerate the design of an enterprise data warehouse business intelligence (BI) solution …
Banking DWH model
Retail DWH model® is standard industry data warehouse model applicable for retailers and wholesalers, covering traditional Business Intelligence requirements, regulatory requirements …
Teradata Financial Services Industry Data Model
The Teradata Financial Services Industry Data Model is the backbone of the Data Integration Roadmap planning model. The Data Integration Roadmap is a high-level visual planning model …
Teradata Financial Services Data Model Support for Financial …
print for designing a custom financial data warehouse that reflects your business priorities and integrates financial information with your customer and supply chain data.
Building an Effective Data Warehousing for Financial Sector
This article presents the implementation process of a Data Warehouse and a multidimensional analysis of business data for a holding company in the financial sector. The goal is to create a …
Data Warehouse Model for Banking - birdconsulting.eu
• The most complex part of the model that supports all types of transactions, financial or non-financial • Also includes information about channels used to generate or settle the transaction
IBM Support and downloads
The IBM Banking Data Warehouse Model (BDWM) is a comprehensive logical data model containing the structures required to store all financial services data in an efficient layout. • …
A FINANCIAL SERVICES LEADER’S GUIDE TO BUILDING A …
as the final data domains and products—and Deloitte has developed two proprietary data model frameworks to help clients through this process. Both frameworks allow organizations to …
IBM Industry Models for Financial Services
The IBM Banking Data Warehouse content models are the cornerstone components of a financial sercices organizations customized development of a data warehouse and business intelligence …
Complete Data Lifecycle Management with Oracle Financial …
Financial institution achieves core data sourcing with a high level of data model fitment for GL data, credit card systems, and loans systems. Oracle Financial Services Data Foundation …
Performance of Integrated Data Warehouse Architecture for …
The proposed integrated data warehouse for financial institute with security levels has integrated with “Financial Transaction Model” [2], “Data Warehouse Architecture for Financial Institute” [3] …
Beacon’s Data Warehouse and Bi-Temporal Data Model
Beacon’s model for bi-temporal reference data is based on a framework where an object in the Beacon Object Database can point to versioned data based on the environment settings for the …
The IBM Information Framework for the banking industry: …
• The IFW data models, comprising of the Banking Data Warehouse, an information data model that provides a blueprint for a comprehensive data warehouse and analysis templates for use …
Data Warehouse Architecture for Financial Institutes to
proposed data warehouse architecture for financial institute will be well-built to execute a position to augment the present financial core system with BUID. The major advantage of this proposed …
Performance Tuning Techniques for Large-Scale Financial …
In this paper, various performance-tuning strategies that enhance data acquisition, query response, and resource utilization for financial data warehouses are presented.
IBM Industry Models For Financial Services
The Financial Services Data Models (FSDM) comprises an enterprise-wide vocabulary used to precisely define the meaning of the many concepts (e.g. Customer, Product, Channel) that …
IBM Industry Models for Financial Markets
IBM Financial Markets Data Warehouse contains business and design models that accelerate the gathering of business requirements, defining the important business terms and designing the …
Oracle® Financial Services Data Warehouse
The Oracle Financial Services Data Warehouse (OFSDW) is an analytical data warehouse platform for the Financial Services industry. OFSDW combines an industry data model for Financial Services along with a set of management and …
Oracle Financial Services Data Foundation
With a fully physicalized data model, the Oracle Financial Services Data Foundation leverages 3,000 entities and 20,000 elements, which have been modeled for known use cases such as regulatory reporting, financial close process, and risk and …
IBM Industry Models for Banking
IBM Banking Data Warehouse (BDW) comprises interconnected models and supporting tooling that accelerate the design of an enterprise data warehouse business intelligence (BI) solution driven by financial-services-centered business …
IBM Industry Models for Financial Services
Chapter 3: The IFW Banking Data Warehouse Models assist with creating a consistent enterprise view of information. Chapter 4: The IFW Process Models assist with process simplification and business process re-engineering.
Banking DWH model
Retail DWH model® is standard industry data warehouse model applicable for retailers and wholesalers, covering traditional Business Intelligence requirements, regulatory requirements and Big Data Analytics requirements. Telco …
