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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
- Sales Rank: #311294 in Books
- Published on: 2014-12-10
- Released on: 2014-11-26
- Original language: English
- Number of items: 1
- Dimensions: 9.23" h x .86" w x 7.48" l, 1.68 pounds
- Binding: Paperback
- 378 pages
About the Author
Dan has more than 25 years of experience in the Data Warehousing and Business Intelligence field and is internationally known for inventing the Data Vault 1.0 model and the Data Vault 2.0 System of Business Intelligence. He helps business and government organizations around the world to achieve BI excellence by applying his proven knowledge in Big Data, unstructured information management, agile methodologies and product development. He has held training classes and presented at TDWI, Teradata Partners, DAMA, Informatica, Oracle user groups and Data Modeling Zone conference. He has a background in SEI/CMMI Level 5, and has contributed architecture efforts to petabyte scale data warehouses and offers high quality on-line training and consulting services for Data Vault.
Most helpful customer reviews
30 of 33 people found the following review helpful.
Less than a primer, not for data scientist, not really general data architecture, maybe the most mature Data Vault cookbook
By Dominic Roy
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 the territory is not covered.
I would not recommend this book for a university student, a data professional or a data scientist. Just look at the glossary to convince you. A data model is defined as "an abstraction of data". DW 2.0 is defined as "the second-generation data warehouse architecture". MapReduce is defined as "a language for processing Big Data". A relational model is defined as "a form of data where data is normalized". Even Wikipedia can do better than that. Why putting terms in a glossary in a book if the terms are less precisely defined and/or do not help to contextualize the terms with the subject of the book. It leaves a bad taste for the rest of the book (The semantics may be loose, imprecise with many shortcuts and confusion).
This book tries to cover a lot of technologies in very few pages. A very large part is dedicated to Data Vault and it is, as usual, somewhat self-promoting. However, it could be the best book on Data Vault as far as I know.
I recommend that you skip right over the topics you already know and those who aren't the main subject, because the book presents a limited understanding of those topics : data governance, SDLC, CMMI, TQM, methodologies, Sarbanes-Oxley Act, Agile, Analytics, etc. They seem to be there in an attempt to cover all the topics, but it's not convincing. In my opinion, those topics don't have their place in a technical primer.
There is no bibliography at all which is not very good for a primer that is supposed to introduce you to a topic and guide you to more detailed information if you need. It's disturbing that this topic don't have any scientific paper or any serious monography to refer to. Hey those guys are geniuses, they don't need it; I'm sorry even Albert Einstein made mistakes that were corrected by means of reviewed scientific publications.
In summary, it is not a primer, it is closer to a Data Vault cookbook in a data warehouse environment, with an extension on unstructured data that is not bad. Really, it looks like the most mature book on Data Vault, but you'll have to clean the place, make your own experiments and check the coherence before applying it in a major project. Buy the book, discard some sections, put you own bookmarks, strikethrough the parts that are unproved or wrong, rephrase and fill the book with your experimentation notes.
7 of 7 people found the following review helpful.
Primer to Profound
By Dakota
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 using unstructured data for analytics. An understanding of the analysis of nonrepetitive , unstructured, textual data and how that analysis can provide real business value for corporate decision making could prove to be the lynchpin of Big Data usefulness.
We are beginning to hear about textual disambiguation, and this book provides a concise explanation of the concept of extracting and managing text--such as in email, call center data, and notes,--for never-before-seen business value analytics. The significance of contextualizing the text ‘from narrative into an analytical database’ is profound.
Another valuable discussion in the book is the architecture of Big Data and of the Data Warehouse and how these collections of data relate to each other and work together.
From basics to in-depth considerations this book provides value to data scientists, data analysts, marketing and research professional, and business people who want to understand how to realize the inherent value in their corporate data.
4 of 5 people found the following review helpful.
DATA ARCHITECTURE – A PRIMER FOR THE DATA SCIENTIST Elsevier ...
By Bonnie
DATA ARCHITECTURE – A PRIMER FOR THE DATA SCIENTIST Elsevier Morgan Kaufman
This book is not for everyone. If you are looking for a detailed technical book this book is not for you. If you are looking for a rehash of old ideas and concepts that relate to specific subjects such as data warehouse or Big Data then you need to look elsewhere.
Instead this book is an architecture book. (If you are a technician that does not understand or appreciate architecture, then you will find this book sort of unintelligible.) And the breadth and scope of this book is beyond anything found in the literature of computer science.
First and foremost the book addresses corporate data, in its entirety. All other books address some small aspect of corporate data. But this book is unique in that it addresses the broadest perspective of data.
The book handles subjects not found anywhere, such as the fundamental divide between repetitive and non repetitive data. (Hardly any other technical book even recognizes that there is repetitive and non repetitive data.) And the book addresses the vital topic – how does business value relate to repetitive data and non repetitive data. The book makes the profound point that there is an extreme divide between repetitive data and non repetitive data. This divide is called the “Great Divide”.
Another aspect of this book that is found nowhere else is that of the explanation of textual disambiguation. It is through textual disambiguation that context of data is discovered. Unlike NLP which is essentially context free, textual disambiguation focusses in on the importance of context in trying to use text for analytical processing.
A third unique aspect of this book is a discussion of how a data warehouse and a collection of Big Data relate to each other. The Big Data authors dismiss data warehouse as if it were a throwaway. That simply is an incorrect thing to do. Data warehouse and Big Data rightfully have their own place and should coexist with each other.
One of the primary means by which contextualization occurs is through the use of taxonomies. Taxonomies are explained in the book as well.
Another unique feature of this book is the in depth explanation of the data vault concept.
Other interesting concepts include the standard work unit, the role of visualization, the metadata needed to manage the technical environment, the dimensional model, data marts, the relational model, and other important topics.
In a word the concepts of how technology and data are organized at a high level that form the basis of success in the world of the data scientist are featured. Stated differently, a data scientist that does not understand the world that is described in this book cannot succeed as a data scientist. This book is absolutely essential for every data scientist that is a true professional.
This book is for anyone making strategic decisions. This book is not (and does not pretend to be) a deep technical dive into some one or two subjects. This book is not a rehash or regurgitation of older data warehouse concepts. Instead, this book is for anyone who needs to make strategic decisions about data and the future of technology in the organization.
This book is mandatory for anyone calling themselves an architect.
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