William H. Inmon — новинки

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
Discusses the value in Big Data that is often overlooked, non-repetitive data, and why there is significant business value in using it
Shows how to turn textual information into a form that can be analyzed by standard tools
Explains how Big Data fits within an existing systems environment
Presents new opportunities that are afforded by the advent of Big Data
Demystifies the murky waters of repetitive and non-repetitive data in Big Da

Business Metadata: Capturing Enterprise Knowledge,

Data Warehousing has been around for 20 years and has become part of the information technology infrastructure. Data warehousing originally grew in response to the corporate need for information-not data-and it supplies integrated, granular, and historical data to the corporation. There are many kinds of data warehouses, in large part due to evolution and different paths of software and hardware vendors. But DW 2.0, defined by this author in many talks, articles, and his b-eye-network newsletter that reaches 65,000 professionals monthly, is the well-identified and defined next generation data warehouse. The book carries that theme and describes the future of data warehousing that is technologically possible now, 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. The benefit of this for people who are building or using a data warehouse can see what lies ahead, and can 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.

Data warehouses provide a much-needed strategy for organizations to collect, store, and analyze vast amounts of business data. As businesses expand both brick-and-mortar and online activities, the field of data warehousing has become increasingly important. Since it was first published in 1990, W. H. Inmon's Building the Data Warehouse has been the bible of data warehousing-it is the book that launched the data warehousing industry and it remains the preeminent introduction to the subject. This new edition covers the latest developments with this technology, many of which have been pioneered by Inmon himself.
An overview of all the fundamental components of data warehouse systems offers you a refresher of the methods used for data warehouse design; various data warehouse migration strategies; and the technologies that can be applied for loading, indexing, and managing data. In order to bring you up to date, this in-depth guide then features the latest advances in data warehousing.

New chapters cover:
- Understanding methods for handling unstructured data in a data warehouse
- Storing data across multiple storage media
- Examining the pros and cons of relational vs. multidimensional design
- Measuring return on investment in planning data warehouse projects
- Exploring advanced topics, including data monitoring and testing