Data Architecture A Primer for the Data Scientist

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

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  • Author : W.H. Inmon
  • Publisher : Morgan Kaufmann
  • Pages : 378 pages
  • ISBN : 0128020911
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKData Architecture A Primer for the Data Scientist

Data Architecture: A Primer for the Data Scientist

Data Architecture: A Primer for the Data Scientist
  • Author : W.H. Inmon,Daniel Linstedt
  • Publisher : Morgan Kaufmann
  • Release : 26 November 2014
GET THIS BOOKData Architecture: A Primer for the Data Scientist

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

Data Architecture: A Primer for the Data Scientist

Data Architecture: A Primer for the Data Scientist
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  • Publisher : Academic Press
  • Release : 30 April 2019
GET THIS BOOKData Architecture: A Primer for the Data Scientist

Over the past 5 years, the concept of big data has matured, data science has grown exponentially, and data architecture has become a standard part of organizational decision-making. Throughout all this change, the basic principles that shape the architecture of data have remained the same. There remains a need for people to take a look at the "bigger picture" and to understand where their data fit into the grand scheme of things. Data Architecture: A Primer for the Data Scientist, Second

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  • Publisher : John Wiley & Sons
  • Release : 06 March 2017
GET THIS BOOKData Science For Dummies

Discover how data science can help you gain in-depth insight into your business - the easy way! Jobs in data science abound, but few people have the data science skills needed to fill these increasingly important roles. Data Science For Dummies is the perfect starting point for IT professionals and students who want a quick primer on all areas of the expansive data science space. With a focus on business cases, the book explores topics in big data, data science,

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  • Author : W.H. Inmon,Derek Strauss,Genia Neushloss
  • Publisher : Elsevier
  • Release : 28 July 2010
GET THIS BOOKDW 2.0: The Architecture for the Next Generation of Data Warehousing

DW 2.0: The Architecture for the Next Generation of Data Warehousing is the first book on the new generation of data warehouse architecture, DW 2.0, by the father of the data warehouse. The book describes the future of data warehousing that is technologically possible today, 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. This allows people who are

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  • Publisher : Morgan Kaufmann
  • Release : 15 September 2015
GET THIS BOOKBuilding a Scalable Data Warehouse with Data Vault 2.0

The Data Vault was invented by Dan Linstedt at the U.S. Department of Defense, and the standard has been successfully applied to data warehousing projects at organizations of different sizes, from small to large-size corporations. Due to its simplified design, which is adapted from nature, the Data Vault 2.0 standard helps prevent typical data warehousing failures. "Building a Scalable Data Warehouse" covers everything one needs to know to create a scalable data warehouse end to end, including a presentation of

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  • Publisher : Elsevier
  • Release : 09 May 2011
GET THIS BOOKData Architecture

Data Architecture: From Zen to Reality explains the principles underlying data architecture, how data evolves with organizations, and the challenges organizations face in structuring and managing their data. Using a holistic approach to the field of data architecture, the book describes proven methods and technologies to solve the complex issues dealing with data. It covers the various applied areas of data, including data modelling and data model management, data quality, data governance, enterprise information management, database design, data warehousing, and

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Super Charge Your Data Warehouse
  • Author : Dan Linstedt,Kent Graziano
  • Publisher : CreateSpace
  • Release : 01 November 2011
GET THIS BOOKSuper Charge Your Data Warehouse

Do You Know If Your Data Warehouse Flexible, Scalable, Secure and Will It Stand The Test Of Time And Avoid Being Part Of The Dreaded "Life Cycle"? The Data Vault took the Data Warehouse world by storm when it was released in 2001. Some of the world's largest and most complex data warehouse situations understood the value it gave especially with the capabilities of unlimited scaling, flexibility and security. Here is what industry leaders say about the Data Vault "The Data

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  • Author : Dave Knifton
  • Publisher : Paragon Publishing
  • Release : 10 October 2016
GET THIS BOOKThe Data Model Toolkit

Adopting the latest technological and data related innovations has caused many organisations to realise they don’t have a firm grasp on their basic operational data. This is a problem that Logical Data Models are uniquely qualified to help them solve. The realisation of the need to define a Logical Data Model may be driven by any number of reasons including; trying to link Big Data Analytics to operational data, plunging into Digital Marketing, choosing the best SaaS solution, carrying

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Data Lake Architecture
  • Author : Bill Inmon
  • Publisher : Technics Publications
  • Release : 01 April 2016
GET THIS BOOKData Lake Architecture

Organizations invest incredible amounts of time and money obtaining and then storing big data in data stores called data lakes. But how many of these organizations can actually get the data back out in a useable form? Very few can turn the data lake into an information gold mine. Most wind up with garbage dumps. Data Lake Architecture will explain how to build a useful data lake, where data scientists and data analysts can solve business challenges and identify new

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  • Author : Russell Jurney
  • Publisher : "O'Reilly Media, Inc."
  • Release : 15 October 2013
GET THIS BOOKAgile Data Science

Mining big data requires a deep investment in people and time. How can you be sure you’re building the right models? With this hands-on book, you’ll learn a flexible toolset and methodology for building effective analytics applications with Hadoop. Using lightweight tools such as Python, Apache Pig, and the D3.js library, your team will create an agile environment for exploring data, starting with an example application to mine your own email inboxes. You’ll learn an iterative

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  • Publisher : "O'Reilly Media, Inc."
  • Release : 21 February 2019
GET THIS BOOKThe Enterprise Big Data Lake

The data lake is a daring new approach for harnessing the power of big data technology and providing convenient self-service capabilities. But is it right for your company? This book is based on discussions with practitioners and executives from more than a hundred organizations, ranging from data-driven companies such as Google, LinkedIn, and Facebook, to governments and traditional corporate enterprises. You’ll learn what a data lake is, why enterprises need one, and how to build one successfully with the

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  • Author : Jay A. Etchings
  • Publisher : John Wiley & Sons
  • Release : 03 January 2017
GET THIS BOOKStrategies in Biomedical Data Science

An essential guide to healthcare data problems, sources, and solutions Strategies in Biomedical Data Science provides medical professionals with much-needed guidance toward managing the increasing deluge of healthcare data. Beginning with a look at our current top-down methodologies, this book demonstrates the ways in which both technological development and more effective use of current resources can better serve both patient and payer. The discussion explores the aggregation of disparate data sources, current analytics and toolsets, the growing necessity of smart

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  • Author : Martin Kleppmann
  • Publisher : "O'Reilly Media, Inc."
  • Release : 16 March 2017
GET THIS BOOKDesigning Data-Intensive Applications

Data is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords? In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape