| data warehouse - the ultimate guide: Data Warehousing , 2001-04-27 Rapid access to information is a prime requirement in any organization that wants to have a competitive edge in today's fast changing markets. How to retrieve information? How to capture data? How to format it? The answer lies in Data Warehousing. This HOTT Guide will give you access to all the essential information about the newest data storehouse: through articles by expert trendwachters on strategic considerations, how-to reports defining the various ways to extract the data needed for critical business decisions, technical papers clarifying technologies and tools, business cases and key concepts that will provide the reader with a comprehensive overview of a business solution that is already indispensable. |
| data warehouse - the ultimate guide: 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. |
| data warehouse - the ultimate guide: The Data Warehouse Toolkit Ralph Kimball, Margy Ross, 2013-07-01 Updated new edition of Ralph Kimball's groundbreaking book on dimensional modeling for data warehousing and business intelligence! The first edition of Ralph Kimball's The Data Warehouse Toolkit introduced the industry to dimensional modeling, and now his books are considered the most authoritative guides in this space. This new third edition is a complete library of updated dimensional modeling techniques, the most comprehensive collection ever. It covers new and enhanced star schema dimensional modeling patterns, adds two new chapters on ETL techniques, includes new and expanded business matrices for 12 case studies, and more. Authored by Ralph Kimball and Margy Ross, known worldwide as educators, consultants, and influential thought leaders in data warehousing and business intelligence Begins with fundamental design recommendations and progresses through increasingly complex scenarios Presents unique modeling techniques for business applications such as inventory management, procurement, invoicing, accounting, customer relationship management, big data analytics, and more Draws real-world case studies from a variety of industries, including retail sales, financial services, telecommunications, education, health care, insurance, e-commerce, and more Design dimensional databases that are easy to understand and provide fast query response with The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd Edition. |
| data warehouse - the ultimate guide: 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. |
| data warehouse - the ultimate guide: Using the Data Warehouse W. H. Inmon, Richard D. Hackathorn, 1994-07-27 This book describes exactly how to use a data warehouse once it's been constructed. The discussion of how to use information to capture and maintain competitive advantage will be of particular strategic interest to marketing, production, and other line managers. Database professionals will appreciate the tactical advice on this topic. |
| data warehouse - the ultimate guide: 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. |
| data warehouse - the ultimate guide: Oracle Data Warehouse Tuning for 10g Gavin JT Powell, 2011-04-08 This book should satisfy those who want a different perspective than the official Oracle documentation. It will cover all important aspects of a data warehouse while giving the necessary examples to make the reading a lively experience. - Tim Donar, Author and Systems Architect for Enterprise Data WarehousesTuning a data warehouse database focuses on large transactions, mostly requiring what is known as throughput. Throughput is the passing of large amounts of information through a server, network and Internet environment, backwards and forwards, constantly! The ultimate objective of a data warehouse is the production of meaningful and useful reporting, from historical and archived data. The trick is to make the reports print within an acceptable time frame.A data model contains tables and relationships between tables. Tuning a data model involves Normalization and Denormalization. Different approaches are required depending on the application, such as OLTP or a Data Warehouse. Inappropriate database design can make SQL code impossible to tune. Poor data modeling can have a most profound effect on database performance since all SQL code is constructed from the data model.* Takes users beyond basics to critical issues in running most efficient data warehouse applications* Illustrates how to keep data going in and out in the most productive way possible* Focus is placed on Data Warehouse performance tuning |
| data warehouse - the ultimate guide: Mastering Data Warehouse Aggregates Christopher Adamson, 2012-06-27 This is the first book to provide in-depth coverage of star schema aggregates used in dimensional modeling-from selection and design, to loading and usage, to specific tasks and deliverables for implementation projects Covers the principles of aggregate schema design and the pros and cons of various types of commercial solutions for navigating and building aggregates Discusses how to include aggregates in data warehouse development projects that focus on incremental development, iterative builds, and early data loads |
| data warehouse - the ultimate guide: 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. |
| data warehouse - the ultimate guide: 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. |
| data warehouse - the ultimate guide: Mastering Data Warehouse Design Claudia Imhoff, Nicholas Galemmo, Jonathan G. Geiger, 2003-08-19 A cutting-edge response to Ralph Kimball's challenge to thedata warehouse community that answers some tough questions aboutthe effectiveness of the relational approach to datawarehousing Written by one of the best-known exponents of the Bill Inmonapproach to data warehousing Addresses head-on the tough issues raised by Kimball andexplains how to choose the best modeling technique for solvingcommon data warehouse design problems Weighs the pros and cons of relational vs. dimensional modelingtechniques Focuses on tough modeling problems, including creating andmaintaining keys and modeling calendars, hierarchies, transactions,and data quality |
| data warehouse - the ultimate guide: Snowflake Cookbook Hamid Mahmood Qureshi, Hammad Sharif, 2021-02-25 Develop modern solutions with Snowflake's unique architecture and integration capabilities; process bulk and real-time data into a data lake; and leverage time travel, cloning, and data-sharing features to optimize data operations Key Features Build and scale modern data solutions using the all-in-one Snowflake platform Perform advanced cloud analytics for implementing big data and data science solutions Make quicker and better-informed business decisions by uncovering key insights from your data Book Description Snowflake is a unique cloud-based data warehousing platform built from scratch to perform data management on the cloud. This book introduces you to Snowflake's unique architecture, which places it at the forefront of cloud data warehouses. You'll explore the compute model available with Snowflake, and find out how Snowflake allows extensive scaling through the virtual warehouses. You will then learn how to configure a virtual warehouse for optimizing cost and performance. Moving on, you'll get to grips with the data ecosystem and discover how Snowflake integrates with other technologies for staging and loading data. As you progress through the chapters, you will leverage Snowflake's capabilities to process a series of SQL statements using tasks to build data pipelines and find out how you can create modern data solutions and pipelines designed to provide high performance and scalability. You will also get to grips with creating role hierarchies, adding custom roles, and setting default roles for users before covering advanced topics such as data sharing, cloning, and performance optimization. By the end of this Snowflake book, you will be well-versed in Snowflake's architecture for building modern analytical solutions and understand best practices for solving commonly faced problems using practical recipes. What you will learn Get to grips with data warehousing techniques aligned with Snowflake's cloud architecture Broaden your skills as a data warehouse designer to cover the Snowflake ecosystem Transfer skills from on-premise data warehousing to the Snowflake cloud analytics platform Optimize performance and costs associated with a Snowflake solution Stage data on object stores and load it into Snowflake Secure data and share it efficiently for access Manage transactions and extend Snowflake using stored procedures Extend cloud data applications using Spark Connector Who this book is for This book is for data warehouse developers, data analysts, database administrators, and anyone involved in designing, implementing, and optimizing a Snowflake data warehouse. Knowledge of data warehousing and database and cloud concepts will be useful. Basic familiarity with Snowflake is beneficial, but not necessary. |
| data warehouse - the ultimate guide: Oracle Design: The Definitive Guide Dave Ensor, Ian Stevenson, 1997 This book focuses exclusively on Oracle database design. It covers the most up-to-date Oracle issues and technologies, including massively parallel processors, very large databases, data warehouses, client-server, and distributed database. The design advice is detailed and thorough. The book delves deeply into design issues and gives advice that will have a major impact on your database and system performance. |
| data warehouse - the ultimate guide: 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. |
| data warehouse - the ultimate guide: The Biml Book Andy Leonard, Scott Currie, Jacob Alley, Martin Andersson, Peter Avenant, Bill Fellows, Simon Peck, Reeves Smith, Raymond Sondak, Benjamin Weissman, Cathrine Wilhelmsen, 2017-10-30 Learn Business Intelligence Markup Language (Biml) for automating much of the repetitive, manual labor involved in data integration. We teach you how to build frameworks and use advanced Biml features to get more out of SQL Server Integration Services (SSIS), Transact-SQL (T-SQL), and SQL Server Analysis Services (SSAS) than you ever thought possible. The first part of the book starts with the basics—getting your development environment configured, Biml syntax, and scripting essentials. Whether a beginner or a seasoned Biml expert, the next part of the book guides you through the process of using Biml to build a framework that captures both your design patterns and execution management. Design patterns are reusable code blocks that standardize the approach you use to perform certain types of data integration, logging, and other key data functions. Design patterns solve common problems encountered when developing data integration solutions. Because you do not have to build the code from scratch each time, design patterns improve your efficiency as a Biml developer. In addition to leveraging design patterns in your framework, you will learn how to build a robust metadata store and how to package your framework into Biml bundles for deployment within your enterprise. In the last part of the book, we teach you more advanced Biml features and capabilities, such as SSAS development, T-SQL recipes, documentation autogeneration, and Biml troubleshooting. The Biml Book: Provides practical and applicable examples Teaches you how to use Biml to reduce development time while improving quality Takes you through solutions to common data integration and BI challenges What You'll Learn Master the basics of Business Intelligence Markup Language (Biml) Study patterns for automating SSIS package generation Build a Biml Framework Import and transform database schemas Automate generation of scripts and projects Who This Book Is For BI developers wishing to quickly locate previously tested solutions, Microsoft BI specialists, those seeking more information about solution automation and code generation, and practitioners of Data Integration Lifecycle Management (DILM) in the DevOps enterprise |
| data warehouse - the ultimate guide: Kafka: The Definitive Guide Neha Narkhede, Gwen Shapira, Todd Palino, 2017-08-31 Every enterprise application creates data, whether it’s log messages, metrics, user activity, outgoing messages, or something else. And how to move all of this data becomes nearly as important as the data itself. If you’re an application architect, developer, or production engineer new to Apache Kafka, this practical guide shows you how to use this open source streaming platform to handle real-time data feeds. Engineers from Confluent and LinkedIn who are responsible for developing Kafka explain how to deploy production Kafka clusters, write reliable event-driven microservices, and build scalable stream-processing applications with this platform. Through detailed examples, you’ll learn Kafka’s design principles, reliability guarantees, key APIs, and architecture details, including the replication protocol, the controller, and the storage layer. Understand publish-subscribe messaging and how it fits in the big data ecosystem. Explore Kafka producers and consumers for writing and reading messages Understand Kafka patterns and use-case requirements to ensure reliable data delivery Get best practices for building data pipelines and applications with Kafka Manage Kafka in production, and learn to perform monitoring, tuning, and maintenance tasks Learn the most critical metrics among Kafka’s operational measurements Explore how Kafka’s stream delivery capabilities make it a perfect source for stream processing systems |
| data warehouse - the ultimate guide: Building the Data Warehouse W. H. Inmon, 2002-10-01 The data warehousing bible updated for the new millennium Updated and expanded to reflect the many technological advances occurring since the previous edition, this latest edition of the data warehousing bible provides a comprehensive introduction to building data marts, operational data stores, the Corporate Information Factory, exploration warehouses, and Web-enabled warehouses. Written by the father of the data warehouse concept, the book also reviews the unique requirements for supporting e-business and explores various ways in which the traditional data warehouse can be integrated with new technologies to provide enhanced customer service, sales, and support-both online and offline-including near-line data storage techniques. |
| data warehouse - the ultimate guide: Spark: The Definitive Guide Bill Chambers, Matei Zaharia, 2018-02-08 Learn how to use, deploy, and maintain Apache Spark with this comprehensive guide, written by the creators of the open-source cluster-computing framework. With an emphasis on improvements and new features in Spark 2.0, authors Bill Chambers and Matei Zaharia break down Spark topics into distinct sections, each with unique goals. Youâ??ll explore the basic operations and common functions of Sparkâ??s structured APIs, as well as Structured Streaming, a new high-level API for building end-to-end streaming applications. Developers and system administrators will learn the fundamentals of monitoring, tuning, and debugging Spark, and explore machine learning techniques and scenarios for employing MLlib, Sparkâ??s scalable machine-learning library. Get a gentle overview of big data and Spark Learn about DataFrames, SQL, and Datasetsâ??Sparkâ??s core APIsâ??through worked examples Dive into Sparkâ??s low-level APIs, RDDs, and execution of SQL and DataFrames Understand how Spark runs on a cluster Debug, monitor, and tune Spark clusters and applications Learn the power of Structured Streaming, Sparkâ??s stream-processing engine Learn how you can apply MLlib to a variety of problems, including classification or recommendation |
| data warehouse - the ultimate guide: Data Science Herbert Jones, 2020-01-03 2 comprehensive manuscripts in 1 book Data Science: What the Best Data Scientists Know About Data Analytics, Data Mining, Statistics, Machine Learning, and Big Data - That You Don't Data Science for Business: Predictive Modeling, Data Mining, Data Analytics, Data Warehousing, Data Visualization, Regression Analysis, Database Querying |
| data warehouse - the ultimate guide: Wiffle® Ball Michael Hermann, Inc, 2010 Author Michael Hermann, president of Wicked Cow Entertainment, and The Wiffle Ball Inc.'s, brand managers, gives readers an inside look at this palatial Wiffle get-up and at the best Wiffle fields around, as well as the down-and-dirty on how in 1953 a down-on-his luck shoe polish salesman and his 16-year-old son concocted the first Wiffle Ball from spare perfume packaging, turning a plastic orb into an American icon. You'll explore The Science of Wiffle, and why a Wiffle Ball, well, waffles. You'll also learn all the how to's: how to build the best field; how to throw the best sinker ball; and how to best tape up a Wiffle Bat. And best of all, major leaguers and A-list celebs will share their fondest and funniest Wiffle memories. |
| data warehouse - the ultimate guide: Introduction to Storage Area Networks Jon Tate, Pall Beck, Hector Hugo Ibarra, Shanmuganathan Kumaravel, Libor Miklas, IBM Redbooks, 2018-10-09 The superabundance of data that is created by today's businesses is making storage a strategic investment priority for companies of all sizes. As storage takes precedence, the following major initiatives emerge: Flatten and converge your network: IBM® takes an open, standards-based approach to implement the latest advances in the flat, converged data center network designs of today. IBM Storage solutions enable clients to deploy a high-speed, low-latency Unified Fabric Architecture. Optimize and automate virtualization: Advanced virtualization awareness reduces the cost and complexity of deploying physical and virtual data center infrastructure. Simplify management: IBM data center networks are easy to deploy, maintain, scale, and virtualize, delivering the foundation of consolidated operations for dynamic infrastructure management. Storage is no longer an afterthought. Too much is at stake. Companies are searching for more ways to efficiently manage expanding volumes of data, and to make that data accessible throughout the enterprise. This demand is propelling the move of storage into the network. Also, the increasing complexity of managing large numbers of storage devices and vast amounts of data is driving greater business value into software and services. With current estimates of the amount of data to be managed and made available increasing at 60% each year, this outlook is where a storage area network (SAN) enters the arena. SANs are the leading storage infrastructure for the global economy of today. SANs offer simplified storage management, scalability, flexibility, and availability; and improved data access, movement, and backup. Welcome to the cognitive era. The smarter data center with the improved economics of IT can be achieved by connecting servers and storage with a high-speed and intelligent network fabric. A smarter data center that hosts IBM Storage solutions can provide an environment that is smarter, faster, greener, open, and easy to manage. This IBM® Redbooks® publication provides an introduction to SAN and Ethernet networking, and how these networks help to achieve a smarter data center. This book is intended for people who are not very familiar with IT, or who are just starting out in the IT world. |
| data warehouse - the ultimate guide: Data Lake Development with Big Data Pradeep Pasupuleti, Beulah Salome Purra, 2015-11-26 Explore architectural approaches to building Data Lakes that ingest, index, manage, and analyze massive amounts of data using Big Data technologies About This Book Comprehend the intricacies of architecting a Data Lake and build a data strategy around your current data architecture Efficiently manage vast amounts of data and deliver it to multiple applications and systems with a high degree of performance and scalability Packed with industry best practices and use-case scenarios to get you up-and-running Who This Book Is For This book is for architects and senior managers who are responsible for building a strategy around their current data architecture, helping them identify the need for a Data Lake implementation in an enterprise context. The reader will need a good knowledge of master data management and information lifecycle management, and experience of Big Data technologies. What You Will Learn Identify the need for a Data Lake in your enterprise context and learn to architect a Data Lake Learn to build various tiers of a Data Lake, such as data intake, management, consumption, and governance, with a focus on practical implementation scenarios Find out the key considerations to be taken into account while building each tier of the Data Lake Understand Hadoop-oriented data transfer mechanism to ingest data in batch, micro-batch, and real-time modes Explore various data integration needs and learn how to perform data enrichment and data transformations using Big Data technologies Enable data discovery on the Data Lake to allow users to discover the data Discover how data is packaged and provisioned for consumption Comprehend the importance of including data governance disciplines while building a Data Lake In Detail A Data Lake is a highly scalable platform for storing huge volumes of multistructured data from disparate sources with centralized data management services. This book explores the potential of Data Lakes and explores architectural approaches to building data lakes that ingest, index, manage, and analyze massive amounts of data using batch and real-time processing frameworks. It guides you on how to go about building a Data Lake that is managed by Hadoop and accessed as required by other Big Data applications. This book will guide readers (using best practices) in developing Data Lake's capabilities. It will focus on architect data governance, security, data quality, data lineage tracking, metadata management, and semantic data tagging. By the end of this book, you will have a good understanding of building a Data Lake for Big Data. Style and approach Data Lake Development with Big Data provides architectural approaches to building a Data Lake. It follows a use case-based approach where practical implementation scenarios of each key component are explained. It also helps you understand how these use cases are implemented in a Data Lake. The chapters are organized in a way that mimics the sequential data flow evidenced in a Data Lake. |
| data warehouse - the ultimate guide: The Enterprise Big Data Lake Alex Gorelik, 2019-02-21 The data lake is a daring new approach for harnessing the power of big data technology and providing convenient self-service capabilities. But is it right for your company? This book is based on discussions with practitioners and executives from more than a hundred organizations, ranging from data-driven companies such as Google, LinkedIn, and Facebook, to governments and traditional corporate enterprises. You’ll learn what a data lake is, why enterprises need one, and how to build one successfully with the best practices in this book. Alex Gorelik, CTO and founder of Waterline Data, explains why old systems and processes can no longer support data needs in the enterprise. Then, in a collection of essays about data lake implementation, you’ll examine data lake initiatives, analytic projects, experiences, and best practices from data experts working in various industries. Get a succinct introduction to data warehousing, big data, and data science Learn various paths enterprises take to build a data lake Explore how to build a self-service model and best practices for providing analysts access to the data Use different methods for architecting your data lake Discover ways to implement a data lake from experts in different industries |
| data warehouse - the ultimate guide: The Data Webhouse Toolkit Ralph Kimball, Richard Merz, 2000-02-03 Ralph's latest book ushers in the second wave of the Internet. . . . Bottom line, this book provides the insight to help companies combine Internet-based business intelligence with the bounty of customer data generated from the internet.--William Schmarzo, Director World Wide Solutions, Sales, and Marketing,IBM NUMA-Q. Receiving over 100 million hits a day, the most popular commercial Websites have an excellent opportunity to collect valuable customer data that can help create better service and improve sales. Companies can use this information to determine buying habits, provide customers with recommendations on new products, and much more. Unfortunately, many companies fail to take full advantage of this deluge of information because they lack the necessary resources to effectively analyze it. In this groundbreaking guide, data warehousing's bestselling author, Ralph Kimball, introduces readers to the Data Webhouse--the marriage of the data warehouse and the Web. If designed and deployed correctly, the Webhouse can become the linchpin of the modern, customer-focused company, providing competitive information essential to managers and strategic decision makers. In this book, Dr. Kimball explains the key elements of the Webhouse and provides detailed guidelines for designing, building, and managing the Webhouse. The results are a business better positioned to stay healthy and competitive. In this book, you'll learn methods for: - Tracking Website user actions - Determining whether a customer is about to switch to a competitor - Determining whether a particular Web ad is working - Capturing data points about customer behavior - Designing the Website to support Webhousing - Building clickstream datamarts - Designing the Webhouse user interface - Managing and scaling the Webhouse The companion Website at www.wiley.com/compbooks/kimball provides updates on Webhouse technologies and techniques, as well as links to related sites and resources. |
| data warehouse - the ultimate guide: The Definitive Guide to Warehousing Scott Keller, Council of Supply Chain Management Professionals, Brian C. Keller, 2014 An authoritative guide to planning, implementing, measuring, and optimizing world-class supply chain warehousing processes, this book explains each warehousing option, basic warehousing storage and handling operations, strategic planning, and the effects of warehousing design and service decisions on total logistics costs and customer service. |
| data warehouse - the ultimate guide: Amazon Redshift Cookbook Shruti Worlikar, Thiyagarajan Arumugam, Harshida Patel, Eugene Kawamoto, 2021-07-23 Discover how to build a cloud-based data warehouse at petabyte-scale that is burstable and built to scale for end-to-end analytical solutions Key FeaturesDiscover how to translate familiar data warehousing concepts into Redshift implementationUse impressive Redshift features to optimize development, productionizing, and operations processesFind out how to use advanced features such as concurrency scaling, Redshift Spectrum, and federated queriesBook Description Amazon Redshift is a fully managed, petabyte-scale AWS cloud data warehousing service. It enables you to build new data warehouse workloads on AWS and migrate on-premises traditional data warehousing platforms to Redshift. This book on Amazon Redshift starts by focusing on Redshift architecture, showing you how to perform database administration tasks on Redshift. You'll then learn how to optimize your data warehouse to quickly execute complex analytic queries against very large datasets. Because of the massive amount of data involved in data warehousing, designing your database for analytical processing lets you take full advantage of Redshift's columnar architecture and managed services. As you advance, you'll discover how to deploy fully automated and highly scalable extract, transform, and load (ETL) processes, which help minimize the operational efforts that you have to invest in managing regular ETL pipelines and ensure the timely and accurate refreshing of your data warehouse. Finally, you'll gain a clear understanding of Redshift use cases, data ingestion, data management, security, and scaling so that you can build a scalable data warehouse platform. By the end of this Redshift book, you'll be able to implement a Redshift-based data analytics solution and have understood the best practice solutions to commonly faced problems. What you will learnUse Amazon Redshift to build petabyte-scale data warehouses that are agile at scaleIntegrate your data warehousing solution with a data lake using purpose-built features and services on AWSBuild end-to-end analytical solutions from data sourcing to consumption with the help of useful recipesLeverage Redshift's comprehensive security capabilities to meet the most demanding business requirementsFocus on architectural insights and rationale when using analytical recipesDiscover best practices for working with big data to operate a fully managed solutionWho this book is for This book is for anyone involved in architecting, implementing, and optimizing an Amazon Redshift data warehouse, such as data warehouse developers, data analysts, database administrators, data engineers, and data scientists. Basic knowledge of data warehousing, database systems, and cloud concepts and familiarity with Redshift will be beneficial. |
| data warehouse - the ultimate guide: The Unified Star Schema Bill Inmon, Francesco Puppini, 2020-10 Master the most agile and resilient design for building analytics applications: the Unified Star Schema (USS) approach. The USS has many benefits over traditional dimensional modeling. Witness the power of the USS as a single star schema that serves as a foundation for all present and future business requirements of your organization. |
| data warehouse - the ultimate guide: The Modern Data Warehouse in Azure Matt How, 2020-06-15 Build a modern data warehouse on Microsoft's Azure Platform that is flexible, adaptable, and fast—fast to snap together, reconfigure, and fast at delivering results to drive good decision making in your business. Gone are the days when data warehousing projects were lumbering dinosaur-style projects that took forever, drained budgets, and produced business intelligence (BI) just in time to tell you what to do 10 years ago. This book will show you how to assemble a data warehouse solution like a jigsaw puzzle by connecting specific Azure technologies that address your own needs and bring value to your business. You will see how to implement a range of architectural patterns using batches, events, and streams for both data lake technology and SQL databases. You will discover how to manage metadata and automation to accelerate the development of your warehouse while establishing resilience at every level. And you will know how to feed downstream analytic solutions such as Power BI and Azure Analysis Services to empower data-driven decision making that drives your business forward toward a pattern of success. This book teaches you how to employ the Azure platform in a strategy to dramatically improve implementation speed and flexibility of data warehousing systems. You will know how to make correct decisions in design, architecture, and infrastructure such as choosing which type of SQL engine (from at least three options) best meets the needs of your organization. You also will learn about ETL/ELT structure and the vast number of accelerators and patterns that can be used to aid implementation and ensure resilience. Data warehouse developers and architects will find this book a tremendous resource for moving their skills into the future through cloud-based implementations. What You Will LearnChoose the appropriate Azure SQL engine for implementing a given data warehouse Develop smart, reusable ETL/ELT processes that are resilient and easily maintained Automate mundane development tasks through tools such as PowerShell Ensure consistency of data by creating and enforcing data contracts Explore streaming and event-driven architectures for data ingestionCreate advanced staging layers using Azure Data Lake Gen 2 to feed your data warehouse Who This Book Is For Data warehouse or ETL/ELT developers who wish to implement a data warehouse project in the Azure cloud, and developers currently working in on-premise environments who want to move to the cloud, and for developers with Azure experience looking to tighten up their implementation and consolidate their knowledge |
| data warehouse - the ultimate guide: 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 |
| data warehouse - the ultimate guide: American Museum of Natural History American Museum of Natural History, 2001 #NAME? |
| data warehouse - the ultimate guide: Warehouse Management Gwynne Richards, 2011-06-03 Warehouses are an integral link in the modern supply chain, ensuring that the correct product is delivered in the right quantity, in good condition, at the required time, and at minimal cost: in effect, the perfect order. The effective management of warehouses is vital in minimizing costs and ensuring the efficient operation of any supply chain. Warehouse Management is a complete guide to best practice in warehouse operations. Covering everything from the latest technological advances to current environmental issues, this book provides an indispensable companion to the modern warehouse. Supported by case studies, the text considers many aspects of warehouse management, including: cost reduction productivity people management warehouse operations With helpful tools, hints and up-to-date information, Warehouse Management provides an invaluable resource for anyone looking to reduce costs and boost productivity. |
| data warehouse - the ultimate guide: A Manager's Guide to Data Warehousing Laura Reeves, 2009-06-24 Aimed at helping business and IT managers clearly communicate with each other, this helpful book addresses concerns straight-on and provides practical methods to building a collaborative data warehouse . You’ll get clear explanations of the goals and objectives of each stage of the data warehouse lifecycle while learning the roles that both business managers and technicians play at each stage. Discussions of the most critical decision points for success at each phase of the data warehouse lifecycle help you understand ways in which both business and IT management can make decisions that best meet unified objectives. |
| data warehouse - the ultimate guide: The Textual Warehouse Bill Inmon, Ranjeet Srivastava, 2021-07-12 Build a Textual Warehouse to help your organization understand and analyze documents through text analytics (both sentiment and non-sentiment analysis), to make better business decisions. Learn the important role of documents and text within your organization, the difference between identifying and qualifying text, and when you need document preprocessing. Appreciate the power of taxonomies and the necessity of textual ETL. Know how the textual warehouse architecture differs from the conventional data warehouse architecture and when to apply contextualization and textual disambiguation. About Bill Bill Inmon, the father of the data warehouse, has written 60 books published in nine languages. ComputerWorld named Bill one of the ten most influential people in the history of the computer profession. Bill's latest adventure is the building of technology known as textual disambiguation. About Ranjeet Ranjeet Srivastava is a data management professional and an enterprise architect with more than 20 years in enterprise product research, development, and design of data-intensive mission-critical applications. |
| data warehouse - the ultimate guide: Building a Second Brain Tiago Forte, 2022-06-14 Building a second brain is getting things done for the digital age. It's a ... productivity method for consuming, synthesizing, and remembering the vast amount of information we take in, allowing us to become more effective and creative and harness the unprecedented amount of technology we have at our disposal-- |
| data warehouse - the ultimate guide: Data Warehousing SCN Education B.V., 2013-11-11 Rapid access to information is a prime requirement in any organization that wants to have a competitive edge in today's fast changing markets. How to retrieve information? How to capture data? How to format it? The answer lies in Data Warehousing. This HOTT Guide will give you access to all the essential information about the newest data storehouse: through articles by expert trendwachters on strategic considerations, how-to reports defining the various ways to extract the data needed for critical business decisions, technical papers clarifying technologies and tools, business cases and key concepts that will provide the reader with a comprehensive overview of a business solution that is already indispensable. |
| data warehouse - the ultimate guide: Building the Data Lakehouse Bill Inmon, Ranjeet Srivastava, Mary Levins, 2021-10 The data lakehouse is the next generation of the data warehouse and data lake, designed to meet today's complex and ever-changing analytics, machine learning, and data science requirements. Learn about the features and architecture of the data lakehouse, along with its powerful analytical infrastructure. Appreciate how the universal common connector blends structured, textual, analog, and IoT data. Maintain the lakehouse for future generations through Data Lakehouse Housekeeping and Data Future-proofing. Know how to incorporate the lakehouse into an existing data governance strategy. Incorporate data catalogs, data lineage tools, and open source software into your architecture to ensure your data scientists, analysts, and end users live happily ever after. |
| data warehouse - the ultimate guide: Corporate Information Factory W. H. Inmon, Claudia Imhoff, Ryan Sousa, 2002-03-14 The father of data warehousing incorporates the latesttechnologies into his blueprint for integrated decision supportsystems Today's corporate IT and data warehouse managers are required tomake a small army of technologies work together to ensure fast andaccurate information for business managers. Bill Inmon created theCorporate Information Factory to solve the needs ofthese managers. Since the First Edition, the design of the factoryhas grown and changed dramatically. This Second Edition, revisedand expanded by 40% with five new chapters, incorporates thesechanges. This step-by-step guide will enable readers to connecttheir legacy systems with the data warehouse and deal with a hostof new and changing technologies, including Web access mechanisms,e-commerce systems, ERP (Enterprise Resource Planning) systems. Thebook also looks closely at exploration and data mining servers foranalyzing customer behavior and departmental data marts forfinance, sales, and marketing. |
| data warehouse - the ultimate guide: Entertainment Weekly The Ultimate Guide to Outlander The Editors of Entertainment Weekly, 2018-10-26 Entertainment Weekly Magazine presents Outlander. |
| data warehouse - the ultimate guide: Database Administration Craig Mullins, 2002 Giving comprehensive, soup-to-nuts coverage of database administration, this guide is written from a platform-independent viewpoint, emphasizing best practices. |
| data warehouse - the ultimate guide: SPIN® -Selling Neil Rackham, 2020-04-28 True or false? In selling high-value products or services: 'closing' increases your chance of success; it is essential to describe the benefits of your product or service to the customer; objection handling is an important skill; open questions are more effective than closed questions. All false, says this provocative book. Neil Rackham and his team studied more than 35,000 sales calls made by 10,000 sales people in 23 countries over 12 years. Their findings revealed that many of the methods developed for selling low-value goods just don‘t work for major sales. Rackham went on to introduce his SPIN-Selling method. SPIN describes the whole selling process: Situation questions Problem questions Implication questions Need-payoff questions SPIN-Selling provides you with a set of simple and practical techniques which have been tried in many of today‘s leading companies with dramatic improvements to their sales performance. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a Transnationa…
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; …
Belmont Forum Adopts Open Data Principles for Environmental Chan…
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a Transnationa…
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and …
Belmont Forum Adopts Open Data Principles for Environmental Chan…
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes …
