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| etl process flow diagram ppt: 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 process flow diagram ppt: 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. |
| etl process flow diagram ppt: 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 process flow diagram ppt: 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 process flow diagram ppt: 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. |
| etl process flow diagram ppt: Data Governance John Ladley, 2019-11-08 Managing data continues to grow as a necessity for modern organizations. There are seemingly infinite opportunities for organic growth, reduction of costs, and creation of new products and services. It has become apparent that none of these opportunities can happen smoothly without data governance. The cost of exponential data growth and privacy / security concerns are becoming burdensome. Organizations will encounter unexpected consequences in new sources of risk. The solution to these challenges is also data governance; ensuring balance between risk and opportunity. Data Governance, Second Edition, is for any executive, manager or data professional who needs to understand or implement a data governance program. It is required to ensure consistent, accurate and reliable data across their organization. This book offers an overview of why data governance is needed, how to design, initiate, and execute a program and how to keep the program sustainable. This valuable resource provides comprehensive guidance to beginning professionals, managers or analysts looking to improve their processes, and advanced students in Data Management and related courses. With the provided framework and case studies all professionals in the data governance field will gain key insights into launching successful and money-saving data governance program. - Incorporates industry changes, lessons learned and new approaches - Explores various ways in which data analysts and managers can ensure consistent, accurate and reliable data across their organizations - Includes new case studies which detail real-world situations - Explores all of the capabilities an organization must adopt to become data driven - Provides guidance on various approaches to data governance, to determine whether an organization should be low profile, central controlled, agile, or traditional - Provides guidance on using technology and separating vendor hype from sincere delivery of necessary capabilities - Offers readers insights into how their organizations can improve the value of their data, through data quality, data strategy and data literacy - Provides up to 75% brand-new content compared to the first edition |
| etl process flow diagram ppt: 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 process flow diagram ppt: Data Mesh Zhamak Dehghani, 2022-03-08 Many enterprises are investing in a next-generation data lake, hoping to democratize data at scale to provide business insights and ultimately make automated intelligent decisions. In this practical book, author Zhamak Dehghani reveals that, despite the time, money, and effort poured into them, data warehouses and data lakes fail when applied at the scale and speed of today's organizations. A distributed data mesh is a better choice. Dehghani guides architects, technical leaders, and decision makers on their journey from monolithic big data architecture to a sociotechnical paradigm that draws from modern distributed architecture. A data mesh considers domains as a first-class concern, applies platform thinking to create self-serve data infrastructure, treats data as a product, and introduces a federated and computational model of data governance. This book shows you why and how. Examine the current data landscape from the perspective of business and organizational needs, environmental challenges, and existing architectures Analyze the landscape's underlying characteristics and failure modes Get a complete introduction to data mesh principles and its constituents Learn how to design a data mesh architecture Move beyond a monolithic data lake to a distributed data mesh. |
| etl process flow diagram ppt: Principles of Data Integration AnHai Doan, Alon Halevy, Zachary Ives, 2012-06-25 Principles of Data Integration is the first comprehensive textbook of data integration, covering theoretical principles and implementation issues as well as current challenges raised by the semantic web and cloud computing. The book offers a range of data integration solutions enabling you to focus on what is most relevant to the problem at hand. Readers will also learn how to build their own algorithms and implement their own data integration application. Written by three of the most respected experts in the field, this book provides an extensive introduction to the theory and concepts underlying today's data integration techniques, with detailed, instruction for their application using concrete examples throughout to explain the concepts. This text is an ideal resource for database practitioners in industry, including data warehouse engineers, database system designers, data architects/enterprise architects, database researchers, statisticians, and data analysts; students in data analytics and knowledge discovery; and other data professionals working at the R&D and implementation levels. - Offers a range of data integration solutions enabling you to focus on what is most relevant to the problem at hand - Enables you to build your own algorithms and implement your own data integration applications |
| etl process flow diagram ppt: MITRE Systems Engineering Guide , 2012-06-05 |
| etl process flow diagram ppt: Subsystem and Transaction Monitoring and Tuning with DB2 11 for z/OS Paolo Bruni, Felipe Bortoletto, Adrian Burke, Cathy Drummond, Yasuhiro Ohmori, IBM Redbooks, 2022-08-31 This IBM® Redbooks® publication discusses in detail the facilities of DB2® for z/OS®, which allow complete monitoring of a DB2 environment. It focuses on the use of the DB2 instrumentation facility component (IFC) to provide monitoring of DB2 data and events and includes suggestions for related tuning. We discuss the collection of statistics for the verification of performance of the various components of the DB2 system and accounting for tracking the behavior of the applications. We have intentionally omitted considerations for query optimization; they are worth a separate document. Use this book to activate the right traces to help you monitor the performance of your DB2 system and to tune the various aspects of subsystem and application performance. |
| etl process flow diagram ppt: Registries for Evaluating Patient Outcomes Agency for Healthcare Research and Quality/AHRQ, 2014-04-01 This User’s Guide is intended to support the design, implementation, analysis, interpretation, and quality evaluation of registries created to increase understanding of patient outcomes. For the purposes of this guide, a patient registry is an organized system that uses observational study methods to collect uniform data (clinical and other) to evaluate specified outcomes for a population defined by a particular disease, condition, or exposure, and that serves one or more predetermined scientific, clinical, or policy purposes. A registry database is a file (or files) derived from the registry. Although registries can serve many purposes, this guide focuses on registries created for one or more of the following purposes: to describe the natural history of disease, to determine clinical effectiveness or cost-effectiveness of health care products and services, to measure or monitor safety and harm, and/or to measure quality of care. Registries are classified according to how their populations are defined. For example, product registries include patients who have been exposed to biopharmaceutical products or medical devices. Health services registries consist of patients who have had a common procedure, clinical encounter, or hospitalization. Disease or condition registries are defined by patients having the same diagnosis, such as cystic fibrosis or heart failure. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews. |
| etl process flow diagram ppt: Making Sense of NoSQL Ann Kelly, Dan McCreary, 2013-09-02 Summary Making Sense of NoSQL clearly and concisely explains the concepts, features, benefits, potential, and limitations of NoSQL technologies. Using examples and use cases, illustrations, and plain, jargon-free writing, this guide shows how you can effectively assemble a NoSQL solution to replace or augment the traditional RDBMS you have now. About this Book If you want to understand and perhaps start using the new data storage and analysis technologies that go beyond the SQL database model, this book is for you. Written in plain language suitable for technical managers and developers, and using many examples, use cases, and illustrations, this book explains the concepts, features, benefits, potential, and limitations of NoSQL. Making Sense of NoSQL starts by comparing familiar database concepts to the new NoSQL patterns that augment or replace them. Then, you'll explore case studies on big data, search, reliability, and business agility that apply these new patterns to today's business problems. You'll see how NoSQL systems can leverage the resources of modern cloud computing and multiple-CPU data centers. The final chaptersshow you how to choose the right NoSQL technologies for your own needs. Managers and developers will welcome this lucid overview of the potential and capabilities of NoSQL technologies. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. What's Inside NoSQL data architecture patterns NoSQL for big data Search, high availability, and security Choosing an architecture About the Authors Dan McCreary and Ann Kelly lead an independent training and consultancy firm focused on NoSQL solutions and are cofounders of the NoSQL Now! Conference. Table of Contents PART 1 INTRODUCTION NoSQL: It's about making intelligent choices NoSQL concepts PART 2 DATABASE PATTERNS Foundational data architecture patterns NoSQL data architecture patterns Native XML databases PART 3 NOSQL SOLUTIONS Using NoSQL to manage big data Finding information with NoSQL search Building high-availability solutions with NoSQL Increasing agility with NoSQL PART 4 ADVANCED TOPICS NoSQL and functional programming Security: protecting data in your NoSQL systems Selecting the right NoSQL solution |
| etl process flow diagram ppt: Data Science and Big Data Analytics EMC Education Services, 2014-12-19 Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: Become a contributor on a data science team Deploy a structured lifecycle approach to data analytics problems Apply appropriate analytic techniques and tools to analyzing big data Learn how to tell a compelling story with data to drive business action Prepare for EMC Proven Professional Data Science Certification Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today! |
| etl process flow diagram ppt: Beginning Database Design Gavin Powell, 2006 From the #1 source for computing information, trusted by more than six million readers worldwide. |
| etl process flow diagram ppt: Modern Enterprise Data Pipelines Mike Bachman, Haji Aref, Rick Lemelin, Andrei Paduroiu, 2021-06-25 A Dell Technologies perspective on today's data landscape and the key ingredients for planning a modern, distributed data pipeline for your multicloud data-driven enterprise |
| etl process flow diagram ppt: Google BigQuery: The Definitive Guide Valliappa Lakshmanan, Jordan Tigani, 2019-10-23 Work with petabyte-scale datasets while building a collaborative, agile workplace in the process. This practical book is the canonical reference to Google BigQuery, the query engine that lets you conduct interactive analysis of large datasets. BigQuery enables enterprises to efficiently store, query, ingest, and learn from their data in a convenient framework. With this book, you’ll examine how to analyze data at scale to derive insights from large datasets efficiently. Valliappa Lakshmanan, tech lead for Google Cloud Platform, and Jordan Tigani, engineering director for the BigQuery team, provide best practices for modern data warehousing within an autoscaled, serverless public cloud. Whether you want to explore parts of BigQuery you’re not familiar with or prefer to focus on specific tasks, this reference is indispensable. |
| etl process flow diagram ppt: Python Data Analysis Ivan Idris, 2014-10-28 This book is for programmers, scientists, and engineers who have knowledge of the Python language and know the basics of data science. It is for those who wish to learn different data analysis methods using Python and its libraries. This book contains all the basic ingredients you need to become an expert data analyst. |
| etl process flow diagram ppt: Large-Scale Data Analytics Aris Gkoulalas-Divanis, Abderrahim Labbi, 2014-01-08 This edited book collects state-of-the-art research related to large-scale data analytics that has been accomplished over the last few years. This is among the first books devoted to this important area based on contributions from diverse scientific areas such as databases, data mining, supercomputing, hardware architecture, data visualization, statistics, and privacy. There is increasing need for new approaches and technologies that can analyze and synthesize very large amounts of data, in the order of petabytes, that are generated by massively distributed data sources. This requires new distributed architectures for data analysis. Additionally, the heterogeneity of such sources imposes significant challenges for the efficient analysis of the data under numerous constraints, including consistent data integration, data homogenization and scaling, privacy and security preservation. The authors also broaden reader understanding of emerging real-world applications in domains such as customer behavior modeling, graph mining, telecommunications, cyber-security, and social network analysis, all of which impose extra requirements for large-scale data analysis. Large-Scale Data Analytics is organized in 8 chapters, each providing a survey of an important direction of large-scale data analytics or individual results of the emerging research in the field. The book presents key recent research that will help shape the future of large-scale data analytics, leading the way to the design of new approaches and technologies that can analyze and synthesize very large amounts of heterogeneous data. Students, researchers, professionals and practitioners will find this book an authoritative and comprehensive resource. |
| etl process flow diagram ppt: Web Services and Service-oriented Architectures Douglas K. Barry, 2003 Interesting, timely, and above all, useful, Savvy Guides give IT managers the information they need to effectively manage their technologists, as well as conscientiously inform business decision makers, in the midst of technological revolution. |
| etl process flow diagram ppt: Big Data For Dummies Judith S. Hurwitz, Alan Nugent, Fern Halper, Marcia Kaufman, 2013-04-02 Find the right big data solution for your business or organization Big data management is one of the major challenges facing business, industry, and not-for-profit organizations. Data sets such as customer transactions for a mega-retailer, weather patterns monitored by meteorologists, or social network activity can quickly outpace the capacity of traditional data management tools. If you need to develop or manage big data solutions, you'll appreciate how these four experts define, explain, and guide you through this new and often confusing concept. You'll learn what it is, why it matters, and how to choose and implement solutions that work. Effectively managing big data is an issue of growing importance to businesses, not-for-profit organizations, government, and IT professionals Authors are experts in information management, big data, and a variety of solutions Explains big data in detail and discusses how to select and implement a solution, security concerns to consider, data storage and presentation issues, analytics, and much more Provides essential information in a no-nonsense, easy-to-understand style that is empowering Big Data For Dummies cuts through the confusion and helps you take charge of big data solutions for your organization. |
| etl process flow diagram ppt: DW 2.0: The Architecture for the Next Generation of Data Warehousing W.H. Inmon, Derek Strauss, Genia Neushloss, 2010-07-28 DW 2.0: The Architecture for the Next Generation of Data Warehousing is the first book on the new generation of data warehouse architecture, DW 2.0, by the father of the data warehouse. The book describes the future of data warehousing that is technologically possible today, at both an architectural level and technology level. The perspective of the book is from the top down: looking at the overall architecture and then delving into the issues underlying the components. This allows people who are building or using a data warehouse to see what lies ahead and determine what new technology to buy, how to plan extensions to the data warehouse, what can be salvaged from the current system, and how to justify the expense at the most practical level. This book gives experienced data warehouse professionals everything they need in order to implement the new generation DW 2.0. It is designed for professionals in the IT organization, including data architects, DBAs, systems design and development professionals, as well as data warehouse and knowledge management professionals. - First book on the new generation of data warehouse architecture, DW 2.0 - Written by the father of the data warehouse, Bill Inmon, a columnist and newsletter editor of The Bill Inmon Channel on the Business Intelligence Network - Long overdue comprehensive coverage of the implementation of technology and tools that enable the new generation of the DW: metadata, temporal data, ETL, unstructured data, and data quality control |
| etl process flow diagram ppt: Trino: The Definitive Guide Matt Fuller, Manfred Moser, Martin Traverso, 2021-04-14 Perform fast interactive analytics against different data sources using the Trino high-performance distributed SQL query engine. With this practical guide, you'll learn how to conduct analytics on data where it lives, whether it's Hive, Cassandra, a relational database, or a proprietary data store. Analysts, software engineers, and production engineers will learn how to manage, use, and even develop with Trino. Initially developed by Facebook, open source Trino is now used by Netflix, Airbnb, LinkedIn, Twitter, Uber, and many other companies. Matt Fuller, Manfred Moser, and Martin Traverso show you how a single Trino query can combine data from multiple sources to allow for analytics across your entire organization. Get started: Explore Trino's use cases and learn about tools that will help you connect to Trino and query data Go deeper: Learn Trino's internal workings, including how to connect to and query data sources with support for SQL statements, operators, functions, and more Put Trino in production: Secure Trino, monitor workloads, tune queries, and connect more applications; learn how other organizations apply Trino |
| etl process flow diagram ppt: Lean Manufacturing and Six Sigma Fausto Pedro García Márquez, Isaac Segovia, Tamás Bányai, Péter Tamás, 2020-02-14 Lean Manufacturing, also called lean production, was originally created in Toyota after the Second World War, in the reconstruction period. It is based on the idea of eliminating any waste in the industry, i.e. any activity or task that does not add value and requires resources. It is considered in every level of the industry, e.g. design, manufacturing, distribution, and customer service. The main wastes are: over-production against plan; waiting time of operators and machines; unnecessary transportation; waste in the process itself; excess stock of material and components; non value-adding motion; defects in quality. The diversity of these issues will be covered from algorithms, mathematical models, and software engineering by design methodologies and technical or practical solutions. This book intends to provide the reader with a comprehensive overview of the current state, cases studies, hardware and software solutions, analytics, and data science in dependability engineering. |
| etl process flow diagram ppt: 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. |
| etl process flow diagram ppt: SAP BW: Administration and Performance Optimization Joe Darlak, Jesper Christensen, 2014 As an administrator, your ongoing to-do list dominates your desk. Let us help: With this book, learn the daily, monthly, quarterly, and yearly administration tasks you should be performing, and how to perform them. This comprehensive guide will walk you through system setup and configuration to lay a sound foundation for effective data storage, and then teach you to employ performance tuning techniques to optimize system performance. Consult detailed best practices to learn how to create BW objects and run reports, as well as to effectively use jobs and skills profiles for system support. Advance your SAP BW system from a B-movie to a blockbuster. |
| etl process flow diagram ppt: Applied Predictive Analytics Dean Abbott, 2014-04-14 Learn the art and science of predictive analytics — techniques that get results Predictive analytics is what translates big data into meaningful, usable business information. Written by a leading expert in the field, this guide examines the science of the underlying algorithms as well as the principles and best practices that govern the art of predictive analytics. It clearly explains the theory behind predictive analytics, teaches the methods, principles, and techniques for conducting predictive analytics projects, and offers tips and tricks that are essential for successful predictive modeling. Hands-on examples and case studies are included. The ability to successfully apply predictive analytics enables businesses to effectively interpret big data; essential for competition today This guide teaches not only the principles of predictive analytics, but also how to apply them to achieve real, pragmatic solutions Explains methods, principles, and techniques for conducting predictive analytics projects from start to finish Illustrates each technique with hands-on examples and includes as series of in-depth case studies that apply predictive analytics to common business scenarios A companion website provides all the data sets used to generate the examples as well as a free trial version of software Applied Predictive Analytics arms data and business analysts and business managers with the tools they need to interpret and capitalize on big data. |
| etl process flow diagram ppt: Release It! Michael T. Nygard, 2018-01-08 A single dramatic software failure can cost a company millions of dollars - but can be avoided with simple changes to design and architecture. This new edition of the best-selling industry standard shows you how to create systems that run longer, with fewer failures, and recover better when bad things happen. New coverage includes DevOps, microservices, and cloud-native architecture. Stability antipatterns have grown to include systemic problems in large-scale systems. This is a must-have pragmatic guide to engineering for production systems. If you're a software developer, and you don't want to get alerts every night for the rest of your life, help is here. With a combination of case studies about huge losses - lost revenue, lost reputation, lost time, lost opportunity - and practical, down-to-earth advice that was all gained through painful experience, this book helps you avoid the pitfalls that cost companies millions of dollars in downtime and reputation. Eighty percent of project life-cycle cost is in production, yet few books address this topic. This updated edition deals with the production of today's systems - larger, more complex, and heavily virtualized - and includes information on chaos engineering, the discipline of applying randomness and deliberate stress to reveal systematic problems. Build systems that survive the real world, avoid downtime, implement zero-downtime upgrades and continuous delivery, and make cloud-native applications resilient. Examine ways to architect, design, and build software - particularly distributed systems - that stands up to the typhoon winds of a flash mob, a Slashdotting, or a link on Reddit. Take a hard look at software that failed the test and find ways to make sure your software survives. To skip the pain and get the experience...get this book. |
| etl process flow diagram ppt: Supercharge Power BI MrExcel's Holy Macro! Books, Matt Allington, 2024-09-26 Master the power of DAX and data modeling in Power BI to elevate your data analysis skills. This comprehensive guide covers essential functions, advanced techniques, and practical examples for mastering business analytics. Key Features Comprehensive coverage of DAX functions Step-by-step progression from basics to advanced topics Practical examples to reinforce learning Book DescriptionThis guide is designed to empower Power BI users with advanced skills in data modeling and DAX. It begins with an introduction to the foundational concepts of data modeling, where you'll learn how to structure your data for optimal performance and analysis. You’ll then progress to mastering essential DAX functions, including iterators, filters, and time intelligence. These chapters will help you create sophisticated calculations that bring your data to life. As you advance, the guide delves into more complex topics like evaluation context, context transition, and disconnected tables. These concepts are crucial for understanding how DAX formulas interact with your data, enabling you to build more accurate and insightful reports. The guide also covers practical applications, such as transferring DAX skills to Excel and using advanced Power BI features like Analyze in Excel and Cube Formulas. By the end of this book, you'll have a deep understanding of both data modeling and DAX, equipping you with the knowledge to tackle complex data challenges. Whether you’re working on business intelligence projects or enhancing your data analysis capabilities, this guide will give you the tools to excel in Power BI.What you will learn Create and load data models Master DAX functions Utilize filter propagation Implement time intelligence Transition context efficiently Transfer DAX skills to Excel Who this book is for This book is ideal for data analysts, business intelligence professionals, and Power BI users looking to deepen their understanding of DAX and data modeling. A basic understanding of Power BI and familiarity with data analysis concepts are recommended. |
| etl process flow diagram ppt: Airline Finance Peter S. Morrell, 2019-09-30 Air transport industry finance, with its complexity and special needs such as route rights, airport slots, aircraft leasing options and frequent flyer programmes, requires specific knowledge. While there are numerous financial management and corporate finance texts available, few of these provide explanations for the singularities of the airline industry with worked examples drawn directly from the industry itself. Revised and updated in its third edition, this internationally renowned and respected book provides the essentials to understanding all areas of airline finance. Designed to address each of the distinct areas of financial management in an air transport industry context, it also shows how these fit together, while each chapter and topic provides a detailed resource which can be also consulted separately. Supported at each stage by practical airline examples, it examines the financial trends and prospects for the airline industry as a whole, contrasting the developments for the major regions and airlines. Important techniques in financial analysis are applied to the airline industry, together with critical discussion of key issues. Thoroughly amended and updated throughout, the third edition reflects the many developments that have affected the industry since 2001. It features several important new topics, including Low Cost Carriers (LCCs), fuel hedging and US Chapter 11 provisions. The sections on financial statements and privatisation have been expanded, and a new chapter has been added on equity finance and IPOs. New case studies have been added, as well as the latest available financial data. The range and perspective is even greater than before, with significant expansion of material specific to the US and Asia. The book is a key resource for students of airline management, and a sophisticated and authoritative guide for analysts in financial institutions and consultancies, executives in airlines and related industries, and civil aviation departments. |
| etl process flow diagram ppt: 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 process flow diagram ppt: Getting to Know Web GIS Pinde Fu, 2022-11-15 Get the latest information about online GIS using ArcGIS(R) apps and functionality with Getting to Know Web GIS, fifth edition. |
| etl process flow diagram ppt: IBM Platform Computing Solutions Reference Architectures and Best Practices Dino Quintero, Luis Carlos Cruz, Ricardo Machado Picone, Dusan Smolej, Daniel de Souza Casali, Gheorghe Tudor, Joanna Wong, IBM Redbooks, 2014-09-30 This IBM® Redbooks® publication demonstrates and documents that the combination of IBM System x®, IBM GPFSTM, IBM GPFS-FPO, IBM Platform Symphony®, IBM Platform HPC, IBM Platform LSF®, IBM Platform Cluster Manager Standard Edition, and IBM Platform Cluster Manager Advanced Edition deliver significant value to clients in need of cost-effective, highly scalable, and robust solutions. IBM depth of solutions can help the clients plan a foundation to face challenges in how to manage, maintain, enhance, and provision computing environments to, for example, analyze the growing volumes of data within their organizations. This IBM Redbooks publication addresses topics to educate, reiterate, confirm, and strengthen the widely held opinion of IBM Platform Computing as the systems software platform of choice within an IBM System x environment for deploying and managing environments that help clients solve challenging technical and business problems. This IBM Redbooks publication addresses topics to that help answer customer's complex challenge requirements to manage, maintain, and analyze the growing volumes of data within their organizations and provide expert-level documentation to transfer the how-to-skills to the worldwide support teams. This IBM Redbooks publication is targeted toward technical professionals (consultants, technical support staff, IT Architects, and IT Specialists) who are responsible for delivering cost-effective computing solutions that help optimize business results, product development, and scientific discoveries. |
| etl process flow diagram ppt: Organic Optoelectronic Materials Yongfang Li, 2015-05-30 This volume reviews the latest trends in organic optoelectronic materials. Each comprehensive chapter allows graduate students and newcomers to the field to grasp the basics, whilst also ensuring that they have the most up-to-date overview of the latest research. Topics include: organic conductors and semiconductors; conducting polymers and conjugated polymer semiconductors, as well as their applications in organic field-effect-transistors; organic light-emitting diodes; and organic photovoltaics and transparent conducting electrodes. The molecular structures, synthesis methods, physicochemical and optoelectronic properties of the organic optoelectronic materials are also introduced and described in detail. The authors also elucidate the structures and working mechanisms of organic optoelectronic devices and outline fundamental scientific problems and future research directions. This volume is invaluable to all those interested in organic optoelectronic materials. |
| etl process flow diagram ppt: Dimensional Modeling: In a Business Intelligence Environment Chuck Ballard, Daniel M. Farrell, Amit Gupta, Carlos Mazuela, Stanislav Vohnik, IBM Redbooks, 2012-07-31 In this IBM Redbooks publication we describe and demonstrate dimensional data modeling techniques and technology, specifically focused on business intelligence and data warehousing. It is to help the reader understand how to design, maintain, and use a dimensional model for data warehousing that can provide the data access and performance required for business intelligence. Business intelligence is comprised of a data warehousing infrastructure, and a query, analysis, and reporting environment. Here we focus on the data warehousing infrastructure. But only a specific element of it, the data model - which we consider the base building block of the data warehouse. Or, more precisely, the topic of data modeling and its impact on the business and business applications. The objective is not to provide a treatise on dimensional modeling techniques, but to focus at a more practical level. There is technical content for designing and maintaining such an environment, but also business content. For example, we use case studies to demonstrate how dimensional modeling can impact the business intelligence requirements for your business initiatives. In addition, we provide a detailed discussion on the query aspects of BI and data modeling. For example, we discuss query optimization and how you can determine performance of the data model prior to implementation. You need a solid base for your data warehousing infrastructure . . . . a solid data model. |
| etl process flow diagram ppt: The Farmer's Handbook International Correspondence Schools, 1912 |
| etl process flow diagram ppt: The Self-Service Data Roadmap Sandeep Uttamchandani, 2020-09-10 Data-driven insights are a key competitive advantage for any industry today, but deriving insights from raw data can still take days or weeks. Most organizations can’t scale data science teams fast enough to keep up with the growing amounts of data to transform. What’s the answer? Self-service data. With this practical book, data engineers, data scientists, and team managers will learn how to build a self-service data science platform that helps anyone in your organization extract insights from data. Sandeep Uttamchandani provides a scorecard to track and address bottlenecks that slow down time to insight across data discovery, transformation, processing, and production. This book bridges the gap between data scientists bottlenecked by engineering realities and data engineers unclear about ways to make self-service work. Build a self-service portal to support data discovery, quality, lineage, and governance Select the best approach for each self-service capability using open source cloud technologies Tailor self-service for the people, processes, and technology maturity of your data platform Implement capabilities to democratize data and reduce time to insight Scale your self-service portal to support a large number of users within your organization |
| etl process flow diagram ppt: Implementing CDISC Using SAS Chris Holland, Jack Shostak, 2019-05-30 For decades researchers and programmers have used SAS to analyze, summarize, and report clinical trial data. Now Chris Holland and Jack Shostak have updated their popular Implementing CDISC Using SAS, the first comprehensive book on applying clinical research data and metadata to the Clinical Data Interchange Standards Consortium (CDISC) standards. Implementing CDISC Using SAS: An End-to-End Guide, Revised Second Edition, is an all-inclusive guide on how to implement and analyze the Study Data Tabulation Model (SDTM) and the Analysis Data Model (ADaM) data and prepare clinical trial data for regulatory submission. Updated to reflect the 2017 FDA mandate for adherence to CDISC standards, this new edition covers creating and using metadata, developing conversion specifications, implementing and validating SDTM and ADaM data, determining solutions for legacy data conversions, and preparing data for regulatory submission. The book covers products such as Base SAS, SAS Clinical Data Integration, and the SAS Clinical Standards Toolkit, as well as JMP Clinical. Topics included in this edition include an implementation of the Define-XML 2.0 standard, new SDTM domains, validation with Pinnacle 21 software, event narratives in JMP Clinical, STDM and ADAM metadata spreadsheets, and of course new versions of SAS and JMP software. The second edition was revised to add the latest C-Codes from the most recent release as well as update the make_define macro that accompanies this book in order to add the capability to handle C-Codes. The metadata spreadsheets were updated accordingly. Any manager or user of clinical trial data in this day and age is likely to benefit from knowing how to either put data into a CDISC standard or analyzing and finding data once it is in a CDISC format. If you are one such person--a data manager, clinical and/or statistical programmer, biostatistician, or even a clinician--then this book is for you. |
| etl process flow diagram ppt: Total Quality Management Poorinma M. Charantimath, 2011 |
| etl process flow diagram ppt: The Voynich Manuscript M. E. D'Imperio, 1978 In spite of all the papers that others have written about the manuscript, there is no complete survey of all the approaches, ideas, background information and analytic studies that have accumulated over the nearly fifty-five years since the manuscript was discovered by Wilfrid M. Voynich in 1912. This report pulls together all the information the author could obtain from all the sources she has examined, and to present it in an orderly fashion. The resulting survey will provide a firm basis upon which other students may build their work, whether they seek to decipher the text or simply to learn more about the problem. |
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...
Extract, transform, load - Wikipedia
Extract, transform, load (ETL) is a three-phase computing process where data is extracted from an input source, …
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 …
ETL Process in Data Warehouse - GeeksforGeeks
Mar 27, 2025 · The ETL (Extract, Transform, Load) process plays an important role in data warehousing …
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 …
What is ETL (extract, transform, load)? - IBM
ETL—meaning extract, transform, load—is a data integration process that combines, cleans and organizes data …
