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 Data
Most Helpful Customer Reviews
Putting 'primer' in the title should warn you not to expect too much. Bill Inmon used to deliver more than that.
The problem with a primer is that the authors don't have to justify, exemplify or detail anything. Things are presented like this and you have no place to make a choice. It's not even take it or leave it, it's only take it. I mean most of the things look correct if you apply them and you happen to have the chance to have a situation where it fits. If you don't fit, you have no escape. A primer should present only clear simple concepts that are recognized throughout the community and ALL the concepts pertinent to the title. Imagine a data warehouse book where slow changing dimension is not mentioned, nor bitemporality, CWM, metamodel. OLAP is only mentioned in the glossary. Imagine a data architecture book where the words cartesian, constraints, enumeration or domain are not used. Even conceptual model is not used in the standard meaning. Those are cues that all...
A Primer can be defined as an introductory book – an informative piece of writing and a precursor to what knowledge is to come. This book is written in a clear, straightforward style that presents ‘a brief history of’ and ‘what is’ Data, Big Data, Data Warehouse, and Data Architecture, and Data Vault. And then goes forward to address what is happening now, misconceptions and confusions that exist in concepts of Big Data and analytics, and the need to integrate Corporate Data to realize real business value. The book introduces concepts of structured and unstructured, repetitive and non-repetitive data – constructs which will be increasingly important in the world of Big Data analytics. The majority of corporate decisions are made based on structured data. It’s easy to automate and fits well into standard database technology. This book suggests that the future of the business value proposition of Big Data is dependent on extracting and...
Unlike other books on the subject of data warehouse, this book looks at the broadest perspective of data across the corporation. The most important feature of this book is the connection made between business value and repetitive and non-repetitive data. The fact of the matter is that the vast majority of business value is found in non-repetitive data. Then the book goes on to describe how to actualize business value out of non-repetitive data by textual disambiguation.
Another extremely valuable aspect of the book is the discussion of the larger architecture that entails both Big Data and the data warehouse. A central part of the architecture is the recognition of context as the discriminating feature of textual data flowing into Big Data.
This book contains information fundamental to data scientists, students, and managers of data architecture.
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