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| etl source to target mapping template: Business Intelligence Roadmap Larissa Terpeluk Moss, S. Atre, 2003 This software will enable the user to learn about business intelligence roadmap. |
| etl source to target mapping template: Data Mapping for Data Warehouse Design Qamar Shahbaz, 2015-12-08 Data mapping in a data warehouse is the process of creating a link between two distinct data models' (source and target) tables/attributes. Data mapping is required at many stages of DW life-cycle to help save processor overhead; every stage has its own unique requirements and challenges. Therefore, many data warehouse professionals want to learn data mapping in order to move from an ETL (extract, transform, and load data between databases) developer to a data modeler role. Data Mapping for Data Warehouse Design provides basic and advanced knowledge about business intelligence and data warehouse concepts including real life scenarios that apply the standard techniques to projects across various domains. After reading this book, readers will understand the importance of data mapping across the data warehouse life cycle. - Covers all stages of data warehousing and the role of data mapping in each - Includes a data mapping strategy and techniques that can be applied to many situations - Based on the author's years of real-world experience designing solutions |
| etl source to target mapping template: The Microsoft Data Warehouse Toolkit Joy Mundy, Warren Thornthwaite, 2011-03-08 Best practices and invaluable advice from world-renowned data warehouse experts In this book, leading data warehouse experts from the Kimball Group share best practices for using the upcoming “Business Intelligence release” of SQL Server, referred to as SQL Server 2008 R2. In this new edition, the authors explain how SQL Server 2008 R2 provides a collection of powerful new tools that extend the power of its BI toolset to Excel and SharePoint users and they show how to use SQL Server to build a successful data warehouse that supports the business intelligence requirements that are common to most organizations. Covering the complete suite of data warehousing and BI tools that are part of SQL Server 2008 R2, as well as Microsoft Office, the authors walk you through a full project lifecycle, including design, development, deployment and maintenance. Features more than 50 percent new and revised material that covers the rich new feature set of the SQL Server 2008 R2 release, as well as the Office 2010 release Includes brand new content that focuses on PowerPivot for Excel and SharePoint, Master Data Services, and discusses updated capabilities of SQL Server Analysis, Integration, and Reporting Services Shares detailed case examples that clearly illustrate how to best apply the techniques described in the book The accompanying Web site contains all code samples as well as the sample database used throughout the case studies The Microsoft Data Warehouse Toolkit, Second Edition provides you with the knowledge of how and when to use BI tools such as Analysis Services and Integration Services to accomplish your most essential data warehousing tasks. |
| etl source to target mapping template: The Kimball Group Reader Ralph Kimball, Margy Ross, 2010-03-11 An unparalleled collection of recommended guidelines for data warehousing and business intelligence pioneered by Ralph Kimball and his team of colleagues from the Kimball Group. Recognized and respected throughout the world as the most influential leaders in the data warehousing industry, Ralph Kimball and the Kimball Group have written articles covering more than 250 topics that define the field of data warehousing. For the first time, the Kimball Group's incomparable advice, design tips, and best practices have been gathered in this remarkable collection of articles, which spans a decade of data warehousing innovation. Each group of articles is introduced with original commentaries that explain their role in the overall lifecycle methodology developed by the Kimball Group. These practical, hands-on articles are fully updated to reflect current practices and terminology and cover the complete lifecycle—including project planning, requirements gathering, dimensional modeling, ETL, and business intelligence and analytics. This easily referenced collection is nothing less than vital if you are involved with data warehousing or business intelligence in any capacity. |
| etl source to target mapping template: New Trends in Database and Information Systems II Nick Bassiliades, Mirjana Ivanovic, Margita Kon-Popovska, Yannis Manolopoulos, Themis Palpanas, Goce Trajcevski, Athena Vakali, 2014-08-16 This volume contains the papers of 3 workshops and the doctoral consortium, which are organized in the framework of the 18th East-European Conference on Advances in Databases and Information Systems (ADBIS’2014). The 3rd International Workshop on GPUs in Databases (GID’2014) is devoted to subjects related to utilization of Graphics Processing Units in database environments. The use of GPUs in databases has not yet received enough attention from the database community. The intention of the GID workshop is to provide a discussion on popularizing the GPUs and providing a forum for discussion with respect to the GID’s research ideas and their potential to achieve high speedups in many database applications. The 3rd International Workshop on Ontologies Meet Advanced Information Systems (OAIS’2014) has a twofold objective to present: new and challenging issues in the contribution of ontologies for designing high quality information systems, and new research and technological developments which use ontologies all over the life cycle of information systems. The 1st International Workshop on Technologies for Quality Management in Challenging Applications (TQMCA’2014) focuses on quality management and its importance in new fields such as big data, crowd-sourcing, and stream databases. The Workshop has addressed the need to develop novel approaches and technologies, and to entirely integrate quality management into information system management. |
| etl source to target mapping template: The Microsoft Data Warehouse Toolkit Joy Mundy, Warren Thornthwaite, 2007-03-22 This groundbreaking book is the first in the Kimball Toolkit series to be product-specific. Microsoft’s BI toolset has undergone significant changes in the SQL Server 2005 development cycle. SQL Server 2005 is the first viable, full-functioned data warehouse and business intelligence platform to be offered at a price that will make data warehousing and business intelligence available to a broad set of organizations. This book is meant to offer practical techniques to guide those organizations through the myriad of challenges to true success as measured by contribution to business value. Building a data warehousing and business intelligence system is a complex business and engineering effort. While there are significant technical challenges to overcome in successfully deploying a data warehouse, the authors find that the most common reason for data warehouse project failure is insufficient focus on the business users and business problems. In an effort to help people gain success, this book takes the proven Business Dimensional Lifecycle approach first described in best selling The Data Warehouse Lifecycle Toolkit and applies it to the Microsoft SQL Server 2005 tool set. Beginning with a thorough description of how to gather business requirements, the book then works through the details of creating the target dimensional model, setting up the data warehouse infrastructure, creating the relational atomic database, creating the analysis services databases, designing and building the standard report set, implementing security, dealing with metadata, managing ongoing maintenance and growing the DW/BI system. All of these steps tie back to the business requirements. Each chapter describes the practical steps in the context of the SQL Server 2005 platform. Intended Audience The target audience for this book is the IT department or service provider (consultant) who is: Planning a small to mid-range data warehouse project; Evaluating or planning to use Microsoft technologies as the primary or exclusive data warehouse server technology; Familiar with the general concepts of data warehousing and business intelligence. The book will be directed primarily at the project leader and the warehouse developers, although everyone involved with a data warehouse project will find the book useful. Some of the book’s content will be more technical than the typical project leader will need; other chapters and sections will focus on business issues that are interesting to a database administrator or programmer as guiding information. The book is focused on the mass market, where the volume of data in a single application or data mart is less than 500 GB of raw data. While the book does discuss issues around handling larger warehouses in the Microsoft environment, it is not exclusively, or even primarily, concerned with the unusual challenges of extremely large datasets. About the Authors JOY MUNDY has focused on data warehousing and business intelligence since the early 1990s, specializing in business requirements analysis, dimensional modeling, and business intelligence systems architecture. Joy co-founded InfoDynamics LLC, a data warehouse consulting firm, then joined Microsoft WebTV to develop closed-loop analytic applications and a packaged data warehouse. Before returning to consulting with the Kimball Group in 2004, Joy worked in Microsoft SQL Server product development, managing a team that developed the best practices for building business intelligence systems on the Microsoft platform. Joy began her career as a business analyst in banking and finance. She graduated from Tufts University with a BA in Economics, and from Stanford with an MS in Engineering Economic Systems. WARREN THORNTHWAITE has been building data warehousing and business intelligence systems since 1980. Warren worked at Metaphor for eight years, where he managed the consulting organization and implemented many major data warehouse systems. After Metaphor, Warren managed the enterprise-wide data warehouse development at Stanford University. He then co-founded InfoDynamics LLC, a data warehouse consulting firm, with his co-author, Joy Mundy. Warren joined up with WebTV to help build a world class, multi-terabyte customer focused data warehouse before returning to consulting with the Kimball Group. In addition to designing data warehouses for a range of industries, Warren speaks at major industry conferences and for leading vendors, and is a long-time instructor for Kimball University. Warren holds an MBA in Decision Sciences from the University of Pennsylvania's Wharton School, and a BA in Communications Studies from the University of Michigan. RALPH KIMBALL, PH.D., has been a leading visionary in the data warehouse industry since 1982 and is one of today's most internationally well-known authors, speakers, consultants, and teachers on data warehousing. He writes the Data Warehouse Architect column for Intelligent Enterprise (formerly DBMS) magazine. |
| etl source to target mapping template: Building a Data Integration Team Jarrett Goldfedder, 2020-02-27 Find the right people with the right skills. This book clarifies best practices for creating high-functioning data integration teams, enabling you to understand the skills and requirements, documents, and solutions for planning, designing, and monitoring both one-time migration and daily integration systems. The growth of data is exploding. With multiple sources of information constantly arriving across enterprise systems, combining these systems into a single, cohesive, and documentable unit has become more important than ever. But the approach toward integration is much different than in other software disciplines, requiring the ability to code, collaborate, and disentangle complex business rules into a scalable model. Data migrations and integrations can be complicated. In many cases, project teams save the actual migration for the last weekend of the project, and any issues can lead to missed deadlines or, at worst, corrupted data that needs to be reconciled post-deployment. This book details how to plan strategically to avoid these last-minute risks as well as how to build the right solutions for future integration projects. What You Will Learn Understand the “language” of integrations and how they relate in terms of priority and ownershipCreate valuable documents that lead your team from discovery to deploymentResearch the most important integration tools in the market todayMonitor your error logs and see how the output increases the cycle of continuous improvementMarket across the enterprise to provide valuable integration solutions Who This Book Is For The executive and integration team leaders who are building the corresponding practice. It is also for integration architects, developers, and business analysts who need additional familiarity with ETL tools, integration processes, and associated project deliverables. |
| etl source to target mapping template: Agile Data Warehousing for the Enterprise Ralph Hughes, 2015-09-19 Building upon his earlier book that detailed agile data warehousing programming techniques for the Scrum master, Ralph's latest work illustrates the agile interpretations of the remaining software engineering disciplines: - Requirements management benefits from streamlined templates that not only define projects quickly, but ensure nothing essential is overlooked. - Data engineering receives two new hyper modeling techniques, yielding data warehouses that can be easily adapted when requirements change without having to invest in ruinously expensive data-conversion programs. - Quality assurance advances with not only a stereoscopic top-down and bottom-up planning method, but also the incorporation of the latest in automated test engines. Use this step-by-step guide to deepen your own application development skills through self-study, show your teammates the world's fastest and most reliable techniques for creating business intelligence systems, or ensure that the IT department working for you is building your next decision support system the right way. - Learn how to quickly define scope and architecture before programming starts - Includes techniques of process and data engineering that enable iterative and incremental delivery - Demonstrates how to plan and execute quality assurance plans and includes a guide to continuous integration and automated regression testing - Presents program management strategies for coordinating multiple agile data mart projects so that over time an enterprise data warehouse emerges - Use the provided 120-day road map to establish a robust, agile data warehousing program |
| etl source to target mapping template: Re-conceptualizing Enterprise Information Systems Charles Møller, Sohail Chaudhry, 2012-03-17 This book constitutes the post conference proceedings of the 5th International IFIP Working Conference on Research and Practical Issues of Enterprise Information Systems (CONFENIS 2011), held in Aalborg, Denmark, October 16-18, 2011. The 12 papers presented in this volume were carefully reviewed and selected from 103 submissions. The papers are organized in four sections on conceptualizing enterprise information systems; emerging topics in enterprise information systems; enterprise information systems as a service; and new perspectives on enterprise information systems. These papers are complemented by two keynotes and a short summary of the co-located Workshop on Future Enterprise Information Systems using Lego Serious Games. |
| etl source to target mapping template: Executing Data Quality Projects Danette McGilvray, 2021-05-27 Executing Data Quality Projects, Second Edition presents a structured yet flexible approach for creating, improving, sustaining and managing the quality of data and information within any organization. Studies show that data quality problems are costing businesses billions of dollars each year, with poor data linked to waste and inefficiency, damaged credibility among customers and suppliers, and an organizational inability to make sound decisions. Help is here! This book describes a proven Ten Step approach that combines a conceptual framework for understanding information quality with techniques, tools, and instructions for practically putting the approach to work – with the end result of high-quality trusted data and information, so critical to today's data-dependent organizations. The Ten Steps approach applies to all types of data and all types of organizations – for-profit in any industry, non-profit, government, education, healthcare, science, research, and medicine. This book includes numerous templates, detailed examples, and practical advice for executing every step. At the same time, readers are advised on how to select relevant steps and apply them in different ways to best address the many situations they will face. The layout allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, best practices, and warnings. The experience of actual clients and users of the Ten Steps provide real examples of outputs for the steps plus highlighted, sidebar case studies called Ten Steps in Action. This book uses projects as the vehicle for data quality work and the word broadly to include: 1) focused data quality improvement projects, such as improving data used in supply chain management, 2) data quality activities in other projects such as building new applications and migrating data from legacy systems, integrating data because of mergers and acquisitions, or untangling data due to organizational breakups, and 3) ad hoc use of data quality steps, techniques, or activities in the course of daily work. The Ten Steps approach can also be used to enrich an organization's standard SDLC (whether sequential or Agile) and it complements general improvement methodologies such as six sigma or lean. No two data quality projects are the same but the flexible nature of the Ten Steps means the methodology can be applied to all. The new Second Edition highlights topics such as artificial intelligence and machine learning, Internet of Things, security and privacy, analytics, legal and regulatory requirements, data science, big data, data lakes, and cloud computing, among others, to show their dependence on data and information and why data quality is more relevant and critical now than ever before. - Includes concrete instructions, numerous templates, and practical advice for executing every step of The Ten Steps approach - Contains real examples from around the world, gleaned from the author's consulting practice and from those who implemented based on her training courses and the earlier edition of the book - Allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, and best practices - A companion Web site includes links to numerous data quality resources, including many of the templates featured in the text, quick summaries of key ideas from the Ten Steps methodology, and other tools and information that are available online |
| etl source to target mapping template: SAS Data Integration Studio 3.4 SAS Institute, 2007 This manual is a task-oriented introduction to the main features of SAS Data Integration Studio. SAS Data Integration Studio is a visual design tool that enables you to consolidate and manage enterprise data from a variety of source systems, applications, and technologies. The audience for this manual is users who are responsible for data integration and who have a working knowledge of Base SAS software. This title is also available online. |
| etl source to target mapping template: Metadata Management with IBM InfoSphere Information Server Wei-Dong Zhu, Tuvia Alon, Gregory Arkus, Randy Duran, Marc Haber, Robert Liebke, Frank Morreale Jr., Itzhak Roth, Alan Sumano, IBM Redbooks, 2011-10-18 What do you know about your data? And how do you know what you know about your data? Information governance initiatives address corporate concerns about the quality and reliability of information in planning and decision-making processes. Metadata management refers to the tools, processes, and environment that are provided so that organizations can reliably and easily share, locate, and retrieve information from these systems. Enterprise-wide information integration projects integrate data from these systems to one location to generate required reports and analysis. During this type of implementation process, metadata management must be provided along each step to ensure that the final reports and analysis are from the right data sources, are complete, and have quality. This IBM® Redbooks® publication introduces the information governance initiative and highlights the immediate needs for metadata management. It explains how IBM InfoSphereTM Information Server provides a single unified platform and a collection of product modules and components so that organizations can understand, cleanse, transform, and deliver trustworthy and context-rich information. It describes a typical implementation process. It explains how InfoSphere Information Server provides the functions that are required to implement such a solution and, more importantly, to achieve metadata management. This book is for business leaders and IT architects with an overview of metadata management in information integration solution space. It also provides key technical details that IT professionals can use in a solution planning, design, and implementation process. |
| etl source to target mapping template: 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 |
| etl source to target mapping template: InfoSphere DataStage Parallel Framework Standard Practices Julius Lerm, Paul Christensen, IBM Redbooks, 2013-02-12 In this IBM® Redbooks® publication, we present guidelines for the development of highly efficient and scalable information integration applications with InfoSphereTM DataStage® (DS) parallel jobs. InfoSphere DataStage is at the core of IBM Information Server, providing components that yield a high degree of freedom. For any particular problem there might be multiple solutions, which tend to be influenced by personal preferences, background, and previous experience. All too often, those solutions yield less than optimal, and non-scalable, implementations. This book includes a comprehensive detailed description of the components available, and descriptions on how to use them to obtain scalable and efficient solutions, for both batch and real-time scenarios. The advice provided in this document is the result of the combined proven experience from a number of expert practitioners in the field of high performance information integration, evolved over several years. This book is intended for IT architects, Information Management specialists, and Information Integration specialists responsible for delivering cost-effective IBM InfoSphere DataStage performance on all platforms. |
| etl source to target mapping template: The Kimball Group Reader Ralph Kimball, Margy Ross, 2016-02-01 The final edition of the incomparable data warehousing and business intelligence reference, updated and expanded The Kimball Group Reader, Remastered Collection is the essential reference for data warehouse and business intelligence design, packed with best practices, design tips, and valuable insight from industry pioneer Ralph Kimball and the Kimball Group. This Remastered Collection represents decades of expert advice and mentoring in data warehousing and business intelligence, and is the final work to be published by the Kimball Group. Organized for quick navigation and easy reference, this book contains nearly 20 years of experience on more than 300 topics, all fully up-to-date and expanded with 65 new articles. The discussion covers the complete data warehouse/business intelligence lifecycle, including project planning, requirements gathering, system architecture, dimensional modeling, ETL, and business intelligence analytics, with each group of articles prefaced by original commentaries explaining their role in the overall Kimball Group methodology. Data warehousing/business intelligence industry's current multi-billion dollar value is due in no small part to the contributions of Ralph Kimball and the Kimball Group. Their publications are the standards on which the industry is built, and nearly all data warehouse hardware and software vendors have adopted their methods in one form or another. This book is a compendium of Kimball Group expertise, and an essential reference for anyone in the field. Learn data warehousing and business intelligence from the field's pioneers Get up to date on best practices and essential design tips Gain valuable knowledge on every stage of the project lifecycle Dig into the Kimball Group methodology with hands-on guidance Ralph Kimball and the Kimball Group have continued to refine their methods and techniques based on thousands of hours of consulting and training. This Remastered Collection of The Kimball Group Reader represents their final body of knowledge, and is nothing less than a vital reference for anyone involved in the field. |
| etl source to target mapping template: IBM InfoSphere Information Server Deployment Architectures Chuck Ballard, Tuvia Alon, Naveen Dronavalli, Stephen Jennings, Mark Lee, Sachiko Toratani, IBM Redbooks, 2013-01-17 Typical deployment architectures introduce challenges to fully using the shared metadata platform across products, environments, and servers. Data privacy and information security requirements add even more levels of complexity. IBM® InfoSphere® Information Server provides a comprehensive, metadata-driven platform for delivering trusted information across heterogeneous systems. This IBM Redbooks® publication presents guidelines and criteria for the successful deployment of InfoSphere Information Server components in typical logical infrastructure topologies that use shared metadata capabilities of the platform, and support development lifecycle, data privacy, information security, high availability, and performance requirements. This book can help you evaluate information requirements to determine an appropriate deployment architecture, based on guidelines that are presented here, and that can fulfill specific use cases. It can also help you effectively use the functionality of your Information Server product modules and components to successfully achieve your business goals. This book is for IT architects, information management and integration specialists, and system administrators who are responsible for delivering the full suite of information integration capabilities of InfoSphere Information Server. |
| etl source to target mapping template: The Data Warehouse ETL Toolkit Ralph Kimball, Joe Caserta, 2011-04-27 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 |
| etl source to target mapping template: Generic Model Management Sergey Melnik, 2004-04-28 Many challenging problems in information systems engineering involve the manipulation of complex metadata artifacts or models, such as database schema, interface specifications, or object diagrams, and mappings between models. Applications solving metadata manipulation problems are complex and hard to build. The goal of generic model management is to reduce the amount of programming needed to solve such problems by providing a database infrastructure in which a set of high-level algebraic operators are applied to models and mappings as a whole rather than to their individual building blocks. This book presents a systematic study of the concepts and algorithms for generic model management. The first prototype of a generic model management system is described, the algebraic operators are introduced and analyzed, and novel algorithms for implementing them are developed. Using the prototype system and the operators presented, solutions are developed for several practically relevant problems, such as change propagation and reintegration. |
| etl source to target mapping template: Smarter Business: Dynamic Information with IBM InfoSphere Data Replication CDC Chuck Ballard, Alec Beaton, Mark Ketchie, Frank Ketelaars, Anzar Noor, Judy Parkes, Deepak Rangarao, Bill Shubin, Wim Van Tichelen, IBM Redbooks, 2012-03-12 To make better informed business decisions, better serve clients, and increase operational efficiencies, you must be aware of changes to key data as they occur. In addition, you must enable the immediate delivery of this information to the people and processes that need to act upon it. This ability to sense and respond to data changes is fundamental to dynamic warehousing, master data management, and many other key initiatives. A major challenge in providing this type of environment is determining how to tie all the independent systems together and process the immense data flow requirements. IBM® InfoSphere® Change Data Capture (InfoSphere CDC) can respond to that challenge, providing programming-free data integration, and eliminating redundant data transfer, to minimize the impact on production systems. In this IBM Redbooks® publication, we show you examples of how InfoSphere CDC can be used to implement integrated systems, to keep those systems updated immediately as changes occur, and to use your existing infrastructure and scale up as your workload grows. InfoSphere CDC can also enhance your investment in other software, such as IBM DataStage® and IBM QualityStage®, IBM InfoSphere Warehouse, and IBM InfoSphere Master Data Management Server, enabling real-time and event-driven processes. Enable the integration of your critical data and make it immediately available as your business needs it. |
| etl source to target mapping template: Liquidity Risk Management Shyam Venkat, Stephen Baird, 2016-03-03 The most up-to-date, comprehensive guide on liquidity risk management—from the professionals Written by a team of industry leaders from the Price Waterhouse Coopers Financial Services Regulatory Practice, Liquidity Risk Management is the first book of its kind to pull back the curtain on a global approach to liquidity risk management in the post-financial crisis. Now, as a number of regulatory initiatives emerge, this timely and informative book explores the real-world implications of risk management practices in today's market. Taking a clear and focused approach to the operational and financial obligations of liquidity risk management, the book builds upon a foundational knowledge of banking and capital markets and explores in-depth the key aspects of the subject, including governance, regulatory developments, analytical frameworks, reporting, strategic implications, and more. The book also addresses management practices that are particularly insightful to liquidity risk management practitioners and managers in numerous areas of banking organizations. Each chapter is authored by a Price Waterhouse Coopers partner or director who has significant, hands-on expertise Content addresses key areas of the subject, such as liquidity stress testing and information reporting Several chapters are devoted to Basel III and its implications for bank liquidity risk management and business strategy Includes a dedicated, current, and all-inclusive look at liquidity risk management Complemented with hands-on insight from the field's leading authorities on the subject, Liquidity Risk Management is essential reading for practitioners and managers within banking organizations looking for the most current information on liquidity risk management. |
| etl source to target mapping template: Mastering PostGIS Dominik Mikiewicz, Michal Mackiewicz, Tomasz Nycz, 2017-05-31 Write efficient GIS applications using PostGIS - from data creation to data consumption About This Book Learn how you can use PostGIS for spatial data analysis and manipulation Optimize your queries and build custom functionalities for your GIS application A comprehensive guide with hands-on examples to help you master PostGIS with ease Who This Book Is For If you are a GIS developer or analyst who wants to master PostGIS to build efficient, scalable GIS applications, this book is for you. If you want to conduct advanced analysis of spatial data, this book will also help you. The book assumes that you have a working installation of PostGIS in place, and have working experience with PostgreSQL. What You Will Learn Refresh your knowledge of the PostGIS concepts and spatial databases Solve spatial problems with the use of SQL in real-world scenarios Practical walkthroughs of application development examples using Postgis, GeoServer and OpenLayers. Extract, transform and load your spatial data Expose data directly or through web services. Consume your data in both desktop and web clients In Detail PostGIS is open source extension onf PostgreSQL object-relational database system that allows GIS objects to be stored and allows querying for information and location services. The aim of this book is to help you master the functionalities offered by PostGIS- from data creation, analysis and output, to ETL and live edits. The book begins with an overview of the key concepts related to spatial database systems and how it applies to Spatial RMDS. You will learn to load different formats into your Postgres instance, investigate the spatial nature of your raster data, and finally export it using built-in functionalities or 3th party tools for backup or representational purposes. Through the course of this book, you will be presented with many examples on how to interact with the database using JavaScript and Node.js. Sample web-based applications interacting with backend PostGIS will also be presented throughout the book, so you can get comfortable with the modern ways of consuming and modifying your spatial data. Style and approach This book is a comprehensive guide covering all the concepts you need to master PostGIS. Packed with hands-on examples, tips and tricks, even the most advanced concepts are explained in a very easy-to-follow manner. Every chapter in the book does not only focus on how each task is performed, but also why. |
| etl source to target mapping template: German Medical Data Sciences: Shaping Change - Creative Solutions for Innovative Medicine R. Röhrig, H. Binder, H.-U. Prokosch, 2019-09-25 Healthcare systems have been in a state of flux for a number of years now due to increasing digitalization. Medicine itself is also facing new challenges, and how to maximize the possibilities of artificial intelligence, whether digitalization can help to strengthen patient orientation, and dealing with the issue of data quality and completeness are all issues which require attention, creativity and research. This book presents the proceedings of the 64th annual conference of the German Association for Medical Informatics, Biometry and Epidemiology (GMDS 2019), held in Dortmund, Germany, from 8 - 11 September 2019. The theme of this year’s conference is Shaping Change – Creative Solutions for Innovative Medicine, and the papers presented here focus on active participation in shaping change while ensuring that good scientific practice, evidence and regulation are not lost as a result of innovation. The book is divided into 8 sections: biostatistics; healthcare IT; interoperability - standards, classification, terminology; knowledge engineering and decision support; medical bioinformatics and systems biology; patient centered care; research infrastructure; and sociotechnical systems / usability and evaluation of healthcare IT. The book will be of interest to all those facing the challenges posed by the ongoing revolution in medicine and healthcare. |
| etl source to target mapping template: Electrical Power Systems and Computers Xiaofeng Wan, 2011-06-21 This volume includes extended and revised versions of a set of selected papers from the International Conference on Electric and Electronics (EEIC 2011) , held on June 20-22 , 2011, which is jointly organized by Nanchang University, Springer, and IEEE IAS Nanchang Chapter. The objective of EEIC 2011 Volume 3 is to provide a major interdisciplinary forum for the presentation of new approaches from Electrical Power Systems and Computers, to foster integration of the latest developments in scientific research. 133 related topic papers were selected into this volume. All the papers were reviewed by 2 program committee members and selected by the volume editor Prof. Xiaofeng Wan. We hope every participant can have a good opportunity to exchange their research ideas and results and to discuss the state of the art in the areas of the Electrical Power Systems and Computers. |
| etl source to target mapping template: Business Intelligence Guidebook Rick Sherman, 2014-11-04 Between the high-level concepts of business intelligence and the nitty-gritty instructions for using vendors' tools lies the essential, yet poorly-understood layer of architecture, design and process. Without this knowledge, Big Data is belittled – projects flounder, are late and go over budget. Business Intelligence Guidebook: From Data Integration to Analytics shines a bright light on an often neglected topic, arming you with the knowledge you need to design rock-solid business intelligence and data integration processes. Practicing consultant and adjunct BI professor Rick Sherman takes the guesswork out of creating systems that are cost-effective, reusable and essential for transforming raw data into valuable information for business decision-makers. After reading this book, you will be able to design the overall architecture for functioning business intelligence systems with the supporting data warehousing and data-integration applications. You will have the information you need to get a project launched, developed, managed and delivered on time and on budget – turning the deluge of data into actionable information that fuels business knowledge. Finally, you'll give your career a boost by demonstrating an essential knowledge that puts corporate BI projects on a fast-track to success. - Provides practical guidelines for building successful BI, DW and data integration solutions. - Explains underlying BI, DW and data integration design, architecture and processes in clear, accessible language. - Includes the complete project development lifecycle that can be applied at large enterprises as well as at small to medium-sized businesses - Describes best practices and pragmatic approaches so readers can put them into action. - Companion website includes templates and examples, further discussion of key topics, instructor materials, and references to trusted industry sources. |
| etl source to target mapping template: Developing Data Migrations and Integrations with Salesforce David Masri, 2018-12-18 Migrate your data to Salesforce and build low-maintenance and high-performing data integrations to get the most out of Salesforce and make it a go-to place for all your organization's customer information. When companies choose to roll out Salesforce, users expect it to be the place to find any and all Information related to a customer—the coveted Client 360° view. On the day you go live, users expect to see all their accounts, contacts, and historical data in the system. They also expect that data entered in other systems will be exposed in Salesforce automatically and in a timely manner. This book shows you how to migrate all your legacy data to Salesforce and then design integrations to your organization's mission-critical systems. As the Salesforce platform grows more powerful, it also grows in complexity. Whether you are migrating data to Salesforce, or integrating with Salesforce, it is important to understand how these complexities need to be reflected in your design. Developing Data Migrations and Integrations with Salesforce covers everything you need to know to migrate your data to Salesforce the right way, and how to design low-maintenance, high-performing data integrations with Salesforce. This book is written by a practicing Salesforce integration architect with dozens of Salesforce projects under his belt. The patterns and practices covered in this book are the results of the lessons learned during those projects. What You’ll Learn Know how Salesforce’s data engine is architected and why Use the Salesforce Data APIs to load and extract data Plan and execute your data migration to Salesforce Design low-maintenance, high-performing data integrations with Salesforce Understand common data integration patterns and the pros and cons of each Know real-time integration options for Salesforce Be aware of common pitfalls Build reusable transformation code covering commonly needed Salesforce transformation patterns Who This Book Is For Those tasked with migrating data to Salesforce or building ongoing data integrations with Salesforce, regardless of the ETL tool or middleware chosen; project sponsors or managers nervous about data tracks putting their projects at risk; aspiring Salesforce integration and/or migration specialists; Salesforce developers or architects looking to expand their skills and take on new challenges |
| etl source to target mapping template: 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. |
| etl source to target mapping template: Fundamentals of Data Warehouses Matthias Jarke, Maurizio Lenzerini, Yannis Vassiliou, Panos Vassiliadis, 2013-03-09 This book presents the first comparative review of the state of the art and the best current practices of data warehouses. It covers source and data integration, multidimensional aggregation, query optimization, metadata management, quality assessment, and design optimization. A conceptual framework is presented by which the architecture and quality of a data warehouse can be assessed and improved using enriched metadata management combined with advanced techniques from databases, business modeling, and artificial intelligence. |
| etl source to target mapping template: Smarter Modeling of IBM InfoSphere Master Data Management Solutions Jan-Bernd Bracht, Joerg Rehr, Markus Siebert, Rouven Thimm, IBM Redbooks, 2012-08-09 This IBM® Redbooks® publication presents a development approach for master data management projects, and in particular, those projects based on IBM InfoSphere® MDM Server. The target audience for this book includes Enterprise Architects, Information, Integration and Solution Architects and Designers, Developers, and Product Managers. Master data management combines a set of processes and tools that defines and manages the non-transactional data entities of an organization. Master data management can provide processes for collecting, consolidating, persisting, and distributing this data throughout an organization. IBM InfoSphere Master Data Management Server creates trusted views of master data that can improve applications and business processes. You can use it to gain control over business information by managing and maintaining a complete and accurate view of master data. You also can use InfoSphere MDM Server to extract maximum value from master data by centralizing multiple data domains. InfoSphere MDM Server provides a comprehensive set of prebuilt business services that support a full range of master data management functionality. |
| etl source to target mapping template: SQL Server 2017 Integration Services Cookbook Christian Cote, Matija Lah, Dejan Sarka, 2017-06-30 Harness the power of SQL Server 2017 Integration Services to build your data integration solutions with ease About This Book Acquaint yourself with all the newly introduced features in SQL Server 2017 Integration Services Program and extend your packages to enhance their functionality This detailed, step-by-step guide covers everything you need to develop efficient data integration and data transformation solutions for your organization Who This Book Is For This book is ideal for software engineers, DW/ETL architects, and ETL developers who need to create a new, or enhance an existing, ETL implementation with SQL Server 2017 Integration Services. This book would also be good for individuals who develop ETL solutions that use SSIS and are keen to learn the new features and capabilities in SSIS 2017. What You Will Learn Understand the key components of an ETL solution using SQL Server 2016-2017 Integration Services Design the architecture of a modern ETL solution Have a good knowledge of the new capabilities and features added to Integration Services Implement ETL solutions using Integration Services for both on-premises and Azure data Improve the performance and scalability of an ETL solution Enhance the ETL solution using a custom framework Be able to work on the ETL solution with many other developers and have common design paradigms or techniques Effectively use scripting to solve complex data issues In Detail SQL Server Integration Services is a tool that facilitates data extraction, consolidation, and loading options (ETL), SQL Server coding enhancements, data warehousing, and customizations. With the help of the recipes in this book, you'll gain complete hands-on experience of SSIS 2017 as well as the 2016 new features, design and development improvements including SCD, Tuning, and Customizations. At the start, you'll learn to install and set up SSIS as well other SQL Server resources to make optimal use of this Business Intelligence tools. We'll begin by taking you through the new features in SSIS 2016/2017 and implementing the necessary features to get a modern scalable ETL solution that fits the modern data warehouse. Through the course of chapters, you will learn how to design and build SSIS data warehouses packages using SQL Server Data Tools. Additionally, you'll learn to develop SSIS packages designed to maintain a data warehouse using the Data Flow and other control flow tasks. You'll also be demonstrated many recipes on cleansing data and how to get the end result after applying different transformations. Some real-world scenarios that you might face are also covered and how to handle various issues that you might face when designing your packages. At the end of this book, you'll get to know all the key concepts to perform data integration and transformation. You'll have explored on-premises Big Data integration processes to create a classic data warehouse, and will know how to extend the toolbox with custom tasks and transforms. Style and approach This cookbook follows a problem-solution approach and tackles all kinds of data integration scenarios by using the capabilities of SQL Server 2016 Integration Services. This book is well supplemented with screenshots, tips, and tricks. Each recipe focuses on a particular task and is written in a very easy-to-follow manner. |
| etl source to target mapping template: PHealth 2022 B. Blobel, B. Yang, M. Giacomini, 2022-11-23 Personalized health technologies offer many benefits. Smart mobile systems, textiles and implants and sensor-controlled medical devices have become important enablers for telemedicine and ubiquitous pervasive health as the next-generation health services, while social media and gamification have added another dimension to pHealth as an eco-system. This book presents the proceedings of pHealth 2022, the 19th in the conference series, held as a hybrid event in Oslo, Norway, from 8 – 10 November 2022. The pHealth 2022 conference attracted experts from many scientific domains and brought together health-service vendor and provider institutions, payer organizations, government departments, academic institutions, professional bodies, and patients and citizen representatives. Topics covered include mobile technologies, micro-nano-bio smart systems, bio-data management and analytics, machine learning, artificial intelligence and robotics for personalized health, the Health Internet of Things (HIoT), systems medicine, public health and virtual care. The book includes 2 keynote papers, 10 invited papers, 20 full papers, and 4 poster papers by 113 authors from 23 countries. All submissions were carefully and critically reviewed by at least two independent experts from a country other than the author’s home country, and additionally by at least one member of the Scientific Program Committee, guaranteeing a high scientific level of the accepted and ultimately published papers. Exploring the enormous potential of pHealth for improvements in medical quality and also for the management of healthcare costs and the provision of a better patient experience, the book will be of interest to all those involved in the development and provision of healthcare. |
| etl source to target mapping template: 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. |
| etl source to target mapping template: Processing and Managing Complex Data for Decision Support Darmont, Jrme, Boussaid, Omar, 2006-03-31 This book provides an overall view of the emerging field of complex data processing, highlighting the similarities between the different data, issues and approaches--Provided by publisher. |
| etl source to target mapping template: Oracle Warehouse Builder 11G R2 Bob Griesemer, 2011-05-16 Extract, Transform, and Load data to build a dynamic, operational data warehouse with Oracle Warehouse Builder 11g R2 with this book and eBook. |
| etl source to target mapping template: Exploratory Data Mining and Data Cleaning Tamraparni Dasu, Theodore Johnson, 2003-08-01 Written for practitioners of data mining, data cleaning and database management. Presents a technical treatment of data quality including process, metrics, tools and algorithms. Focuses on developing an evolving modeling strategy through an iterative data exploration loop and incorporation of domain knowledge. Addresses methods of detecting, quantifying and correcting data quality issues that can have a significant impact on findings and decisions, using commercially available tools as well as new algorithmic approaches. Uses case studies to illustrate applications in real life scenarios. Highlights new approaches and methodologies, such as the DataSphere space partitioning and summary based analysis techniques. Exploratory Data Mining and Data Cleaning will serve as an important reference for serious data analysts who need to analyze large amounts of unfamiliar data, managers of operations databases, and students in undergraduate or graduate level courses dealing with large scale data analys is and data mining. |
| etl source to target mapping template: Implementing an InfoSphere Optim Data Growth Solution Whei-Jen Chen, David Alley, Barbara Brown, Sunil Dravida, Saunnie Dunne, Tom Forlenza, Pamela S Hoffman, Tejinder S Luthra, Rajat Tiwary, Claudio Zancani, IBM Redbooks, 2011-11-09 Today, organizations face tremendous challenges with data explosion and information governance. InfoSphereTM OptimTM solutions solve the data growth problem at the source by managing the enterprise application data. The Optim Data Growth solutions are consistent, scalable solutions that include comprehensive capabilities for managing enterprise application data across applications, databases, operating systems, and hardware platforms. You can align the management of your enterprise application data with your business objectives to improve application service levels, lower costs, and mitigate risk. In this IBM® Redbooks® publication, we describe the IBM InfoSphere Optim Data Growth solutions and a methodology that provides implementation guidance from requirements analysis through deployment and administration planning. We also discuss various implementation topics including system architecture design, sizing, scalability, security, performance, and automation. This book is intended to provide various systems development professionals, Data Solution Architects, Data Administrators, Modelers, Data Analysts, Data Integrators, or anyone who has to analyze or integrate data structures, a broad understanding about IBM InfoSphere Optim Data Growth solutions. By being used in conjunction with the product manuals and online help, this book provides guidance about implementing an optimal solution for managing your enterprise application data. |
| etl source to target mapping template: Pentaho Kettle Solutions Matt Casters, Roland Bouman, Jos van Dongen, 2010-09-02 A complete guide to Pentaho Kettle, the Pentaho Data lntegration toolset for ETL This practical book is a complete guide to installing, configuring, and managing Pentaho Kettle. If you’re a database administrator or developer, you’ll first get up to speed on Kettle basics and how to apply Kettle to create ETL solutions—before progressing to specialized concepts such as clustering, extensibility, and data vault models. Learn how to design and build every phase of an ETL solution. Shows developers and database administrators how to use the open-source Pentaho Kettle for enterprise-level ETL processes (Extracting, Transforming, and Loading data) Assumes no prior knowledge of Kettle or ETL, and brings beginners thoroughly up to speed at their own pace Explains how to get Kettle solutions up and running, then follows the 34 ETL subsystems model, as created by the Kimball Group, to explore the entire ETL lifecycle, including all aspects of data warehousing with Kettle Goes beyond routine tasks to explore how to extend Kettle and scale Kettle solutions using a distributed “cloud” Get the most out of Pentaho Kettle and your data warehousing with this detailed guide—from simple single table data migration to complex multisystem clustered data integration tasks. |
| etl source to target mapping template: Data Provisioning for SAP HANA Megan Cundiff, Vernon Gomes, Russell Lamb, Don Loden, Vinay Suneja, 2018-07-26 Before making data available in SAP HANA, you must standardize, integrate, and secure it--that's where data provisioning comes in. In this guide, you'll learn about each of your options, from SAP HANA-based tools like SDI and SDQ to SAP Data Services and SAP LT Replication Server. Whether you'll be provisioning data in batches or in real-time, you'll understand when to use each tool, its requirements, and how it works. A detailed case study will show you how to establish a successful data provisioning practice-- |
| etl source to target mapping template: Mastering Microsoft Power BI Brett Powell, 2018-03-29 Design, create and manage robust Power BI solutions to gain meaningful business insights Key Features Master all the dashboarding and reporting features of Microsoft Power BI Combine data from multiple sources, create stunning visualizations and publish your reports across multiple platforms A comprehensive guide with real-world use cases and examples demonstrating how you can get the best out of Microsoft Power BI Book DescriptionThis book is intended for business intelligence professionals responsible for the design and development of Power BI content as well as managers, architects and administrators who oversee Power BI projects and deployments. The chapters flow from the planning of a Power BI project through the development and distribution of content to the administration of Power BI for an organization. BI developers will learn how to create sustainable and impactful Power BI datasets, reports, and dashboards. This includes connecting to data sources, shaping and enhancing source data, and developing an analytical data model. Additionally, top report and dashboard design practices are described using features such as Bookmarks and the Power KPI visual. BI managers will learn how Power BI’s tools work together such as with the On-premises data gateway and how content can be staged and securely distributed via Apps. Additionally, both the Power BI Report Server and Power BI Premium are reviewed. By the end of this book, you will be confident in creating effective charts, tables, reports or dashboards for any kind of data using the tools and techniques in Microsoft Power BI.What you will learn Build efficient data retrieval and transformation processes with the Power Query M Language Design scalable, user-friendly DirectQuery and Import Data Models Develop visually rich, immersive, and interactive reports and dashboards Maintain version control and stage deployments across development, test, and production environments Manage and monitor the Power BI Service and the On-premises data gateway Develop a fully on-premise solution with the Power BI Report Server Scale up a Power BI solution via Power BI Premium capacity and migration to Azure Analysis Services or SQL Server Analysis Services Who this book is for Business Intelligence professionals and existing Power BI users looking to master Power BI for all their data visualization and dashboarding needs will find this book to be useful. While understanding of the basic BI concepts is required, some exposure to Microsoft Power BI will be helpful. |
| etl source to target mapping template: Master Data Management David Loshin, 2010-07-28 The key to a successful MDM initiative isn't technology or methods, it's people: the stakeholders in the organization and their complex ownership of the data that the initiative will affect.Master Data Management equips you with a deeply practical, business-focused way of thinking about MDM—an understanding that will greatly enhance your ability to communicate with stakeholders and win their support. Moreover, it will help you deserve their support: you'll master all the details involved in planning and executing an MDM project that leads to measurable improvements in business productivity and effectiveness. - Presents a comprehensive roadmap that you can adapt to any MDM project - Emphasizes the critical goal of maintaining and improving data quality - Provides guidelines for determining which data to master. - Examines special issues relating to master data metadata - Considers a range of MDM architectural styles - Covers the synchronization of master data across the application infrastructure |
| etl source to target mapping template: 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. |
Extract, transform, load - Wikipedia
Extract, transform, load (ETL) is a three-phase computing process where data is extracted from an input source, transformed (including cleaning), …
Extract, transform, load (ETL) - Azure Architecture Center
extract, transform, load (ETL) is a data pipeline used to collect data from various sources. It then transforms the data according to business rules, …
ETL Process in Data Warehouse - GeeksforGeeks
Mar 27, 2025 · The ETL (Extract, Transform, Load) process plays an important role in data warehousing by ensuring seamless integration and …
What is ETL? - Extract Transform Load Explained - A…
Extract, transform, and load (ETL) is the process of combining data from multiple sources into a large, central repository called a data warehouse. …
What is ETL (extract, transform, load)? - IBM
ETL—meaning extract, transform, load—is a data integration process that combines, cleans and organizes data from multiple sources into a single, …
Extract, transform, load - Wikipedia
Extract, transform, load (ETL) is a three-phase computing process where data is extracted from an input source, transformed (including cleaning), and loaded into an output data container. …
Extract, transform, load (ETL) - Azure Architecture Center
extract, transform, load (ETL) is a data pipeline used to collect data from various sources. It then transforms the data according to business rules, and it loads the data into a destination data …
ETL Process in Data Warehouse - GeeksforGeeks
Mar 27, 2025 · The ETL (Extract, Transform, Load) process plays an important role in data warehousing by ensuring seamless integration and preparation of data for analysis. This …
What is ETL? - Extract Transform Load Explained - AWS
Extract, transform, and load (ETL) is the process of combining data from multiple sources into a large, central repository called a data warehouse. ETL uses a set of business rules to clean …
What is ETL (extract, transform, load)? - IBM
ETL—meaning extract, transform, load—is a data integration process that combines, cleans and organizes data from multiple sources into a single, consistent data set for storage in a data …
What is ETL? (Extract Transform Load) - Informatica
ETL stands for extract, transform and load. ETL is a type of data integration process referring to three distinct steps to used to synthesize raw data from it's source to a data warehouse, data …
What is ETL? - Google Cloud
ETL stands for extract, transform, and load and is a traditionally accepted way for organizations to combine data from multiple systems into a single database, data store, data warehouse, or data...
