W. H. Inmon Knihy

This book serves as the definitive guide for SAP NetWeaver BI professionals, offering deep insights into key innovations in user experience, query performance, integrated planning, and enterprise-wide data warehousing. The second edition reflects the growing success of SAP NetWeaver and the embedded Business Intelligence (BI) capabilities in SAP BW version 7.0. Authored by SAP insiders, it provides updated information on utilizing SAP BW for designing, building, deploying, populating, accessing, analyzing, presenting, and administering data. The guide outlines the implementation process organizations undergo when adopting the software, starting with an introduction to BI and SAP NetWeaver, and advancing to information modeling and enterprise data warehouse concepts. Readers will learn to access and deliver meaningful analytics and perform integrated planning functions, alongside insights on warehouse administration, performance, and security. With over 50 percent new or revised content, this edition covers extracting data from online transaction processing systems, storing transformed data for optimal reporting and analysis, and utilizing various Business Explorer tools. Additionally, it includes information on scheduling, monitoring, troubleshooting, and archiving data loads. A companion website offers sample chapters in Wiki format and a blog for discussions about the book and SAP, along with sample code and implementati

Today, the world is trying to create and educate data scientists because of the phenomenon of Big Data. And everyone is looking deeply into this technology. But no one is looking at the larger architectural picture of how Big Data needs to fit within the existing systems (data warehousing systems). Taking a look at the larger picture into which Big Data fits gives the data scientist the necessary context for how pieces of the puzzle should fit together. Most references on Big Data look at only one tiny part of a much larger whole. Until data gathered can be put into an existing framework or architecture it can't be used to its full potential. Data Architecture a Primer for the Data Scientist addresses the larger architectural picture of how Big Data fits with the existing information infrastructure, an essential topic for the data scientist. Drawing upon years of practical experience and using numerous examples and an easy to understand framework. W.H. Inmon, and Daniel Linstedt define the importance of data architecture and how it can be used effectively to harness big data within existing systems. You'll be able to: Turn textual information into a form that can be analyzed by standard tools. Make the connection between analytics and Big Data Understand how Big Data fits within an existing systems environment Conduct analytics on repetitive and non-repetitive data