Implementing a Data Warehouse Using Sas Software Course Notes PDF Download

Author: SAS Institute
Publisher:
ISBN: 9781555447885
Category : Data warehousing
Languages : en
Pages : 409

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Author: SAS Institute
Publisher: Sas Inst
ISBN: 9781590471036
Category :
Languages : en
Pages : 452

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Book Description

Author: Alan C. Elliott
Publisher: John Wiley & Sons
ISBN: 1119901626
Category : Education
Languages : en
Pages : 582

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Book Description
SAS ESSENTIALS Valuable step-by-step introduction to using SAS® statistical software as a foundational approach to data analysis and interpretation Presenting a straightforward introduction from the ground up, SAS® Essentials illustrates SAS using hands-on learning techniques and numerous real-world examples; keeping different experience levels in mind, the highly qualified author team has developed the book over 25 years of teaching introductory SAS courses. This book introduces data manipulation, statistical techniques, and the SAS programming language, including SAS macros, reporting results in tables, and factor analysis, as well as sections on character functions, variable manipulation, and merging, appending, and updating files. It features self-contained chapters to enhance the learning process and includes programming approaches for the latest version of the SAS platform. The Third Edition has been updated with expanded examples, a new chapter introducing PROC SQL as well as new end-of-chapter exercises. The Third Edition also includes a companion website with data sets, additional code, notes on SAS programming, functions, ODS, PROC SQL, and SAS Arrays, along with solutions for instructors. Specific sample topics covered in SAS® Essentials include: Getting data into SAS, reading, writing, and importing data, preparing data for analysis, preparing to use SAS procedures, and controlling output using ODS Techniques for creating, editing, and debugging SAS programs, cleaning up messy data sets, and manipulating data using character, time, and numeric functions Other essential computational skills that are utilized by business, government, and organizations alike, and DATA step for data management Using PROC to analyze data, including evaluating quantitative data, analyzing counts and tables, comparing means using T-Tests, correlation and regression, and analysis of variance, nonparametric analysis, logistic regression, factor analysis, and creating custom graphs and reports. SAS® Essentials is a fundamental study resource for professionals preparing for the SAS Base Certification Exam, as well as an ideal textbook for courses in statistics, data analytics, applied SAS programming, and statistical computer applications.

Author: Peter R. Welbrock
Publisher:
ISBN: 9781580251518
Category : Data warehousing
Languages : en
Pages : 0

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Book Description
What is the key to a successful data warehouse? Strategy, design, implementation! This book skillfully provides a conceptual and working model of a successful data warehouse process that is developed by using SAS software. Written for both the business and technical sides of the house, Welbrock supplies real-life experience with data warehousing, not a theoretical approach. This book delivers the following three-phase strategy for building a data warehouse: 1) Build a conceptual data warehouse that contains metadata about the business elements that are documented in the conceptual warehouse into a technological lexicon. 3) Implement the physical data warehouse. Topics discussed that support the data warehousing process are data modeling, data transformation, multi-dimensional databases, data extraction and storage, warehouse loading, client/server, and SAS/Warehouse Administrator. The Strategy for designing your effective data warehouse is found in these pages!

Author: Olivia Parr-Rud
Publisher: SAS Institute
ISBN: 1629593273
Category : Business & Economics
Languages : en
Pages : 182

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Book Description
This tutorial for data analysts new to SAS Enterprise Guide and SAS Enterprise Miner provides valuable experience using powerful statistical software to complete the kinds of business analytics common to most industries. This beginnner's guide with clear, illustrated, step-by-step instructions will lead you through examples based on business case studies. You will formulate the business objective, manage the data, and perform analyses that you can use to optimize marketing, risk, and customer relationship management, as well as business processes and human resources. Topics include descriptive analysis, predictive modeling and analytics, customer segmentation, market analysis, share-of-wallet analysis, penetration analysis, and business intelligence. --

Author: Monika Wahi
Publisher: Packt Publishing Ltd
ISBN: 1789531187
Category : Computers
Languages : en
Pages : 494

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Book Description
Build a strong foundation in SAS data warehousing by understanding data transformation code and policy, data stewardship and management, interconnectivity between SAS and other warehousing products, and print and web reporting Key FeaturesUnderstand how to use SAS macros for standardizing extract, transform, and load (ETL) protocolsDevelop and use data curation files for effective warehouse managementLearn how to develop and manage ETL, policies, and print and web reports that meet user needsBook Description SAS is used for various functions in the development and maintenance of data warehouses, thanks to its reputation of being able to handle ’big data’. This book will help you learn the pros and cons of storing data in SAS. As you progress, you’ll understand how to document and design extract-transform-load (ETL) protocols for SAS processes. Later, you’ll focus on how the use of SAS arrays and macros can help standardize ETL. The book will also help you examine approaches for serving up data using SAS and explore how connecting SAS to other systems can enhance the data warehouse user’s experience. By the end of this data management book, you will have a fundamental understanding of the roles SAS can play in a warehouse environment, and be able to choose wisely when designing your data warehousing processes involving SAS. What you will learnDevelop efficient ways to manage data input/output (I/O) in SASCreate and manage extract, transform, and load (ETL) code in SASStandardize ETL through macro variables, macros, and arraysIdentify data warehouse users and ensure their needs are metDesign crosswalk and other variables to serve analyst needsMaintain data curation files to improve communication and managementUse the output delivery system (ODS) for print and web reportingConnect other products to SAS to optimize storage and reportingWho this book is for This book is for data architects, managers leading data projects, and programmers or developers using SAS who want to effectively maintain a data lake, data mart, or data warehouse.

Author:
Publisher: Sas Inst
ISBN: 9781590478691
Category : Computers
Languages : en
Pages : 256

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Book Description
The ETL process consists of all the steps necessary to extract data from different locations, transform raw operational data into consistent, high-quality business data, and load the data into a data warehouse. SAS provides all of this with the addition of an easy-to-use, metadata-driven warehouse management environment. This manual explains how to use SAS ETL Studio to specify metadata for sources, such as tables in an operational system; specify metadata for targets--the tables and other data stores in a data warehouse; and create jobs that specify how data is extracted, transformed, and loaded from sources to targets. This manual also summarizes how to set up servers, libraries, and other resources that SAS ETL Studio requires. A data warehouse for a fictional company, Orion Star Sports & Outdoors, is used to illustrate these tasks. The audience for this manual is users who are responsible for implementing data warehouses and who have a working knowledge of Base SAS software.

Author: Alejandro Vaisman
Publisher: Springer
ISBN: 3642546552
Category : Computers
Languages : en
Pages : 639

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Book Description
With this textbook, Vaisman and Zimányi deliver excellent coverage of data warehousing and business intelligence technologies ranging from the most basic principles to recent findings and applications. To this end, their work is structured into three parts. Part I describes “Fundamental Concepts” including multi-dimensional models; conceptual and logical data warehouse design and MDX and SQL/OLAP. Subsequently, Part II details “Implementation and Deployment,” which includes physical data warehouse design; data extraction, transformation, and loading (ETL) and data analytics. Lastly, Part III covers “Advanced Topics” such as spatial data warehouses; trajectory data warehouses; semantic technologies in data warehouses and novel technologies like Map Reduce, column-store databases and in-memory databases. As a key characteristic of the book, most of the topics are presented and illustrated using application tools. Specifically, a case study based on the well-known Northwind database illustrates how the concepts presented in the book can be implemented using Microsoft Analysis Services and Pentaho Business Analytics. All chapters are summarized using review questions and exercises to support comprehensive student learning. Supplemental material to assist instructors using this book as a course text is available at http://cs.ulb.ac.be/DWSDIbook/, including electronic versions of the figures, solutions to all exercises, and a set of slides accompanying each chapter. Overall, students, practitioners and researchers alike will find this book the most comprehensive reference work on data warehouses, with key topics described in a clear and educational style.

Author: Sas
Publisher:
ISBN: 9781555447281
Category :
Languages : en
Pages : 400

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Book Description

Author: Jim Georges
Publisher:
ISBN: 9781590474433
Category : Database management
Languages : en
Pages :

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Book Description
This course introduces programming techniques used by analysts to transform raw data into a form suitable for predictive modeling; teaches you how to extract appropriate data from raw data sources; transform transactions or event data to a form that predictive models can utilize; and effectively incorporate non-numeric data in predictive models as well as manage exceptional and extreme data.