Data Architecture: A Primer for the Data Scientist

Big Data, Data Warehouse and Data Vault
Author: W.H. Inmon,Dan Linstedt
Publisher: Morgan Kaufmann
ISBN: 0128020911
Category: Computers
Page: 378
View: 6652

Continue Reading →

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

Building a Scalable Data Warehouse with Data Vault 2.0


Author: Dan Linstedt,Michael Olschimke
Publisher: Morgan Kaufmann Publishers
ISBN: 9780128025109
Category:
Page: 640
View: 5707

Continue Reading →

" Building a Scalable Data Warehouse with Data Vault 2.0 "covers everything users need to create a scalable data warehouse from scratch, including a presentation of the Data Vault modeling technique, which provides the foundations to create a technical data warehouse layer. In addition, the book presents tactics on how to create the input layer (the stage layer) and the presentation layer (data mart) of the Data Vault 2.0 standard. Drawing upon years of practical experience and using numerous examples and an easy to understand framework, Dan Listedt and Michael Olschimke discuss tactics on how to load each layer using SQL Server Integration Services (SSIS), including automation of the Data Vault loading processes, important data warehouse technologies and practices, and data quality services (DQS) and master data services (MDS) in the context of the data vault architecture. Learn from the inventor of the Data Vault Methodology, Dan LinstedtProvides a complete introduction to data warehousing, applications, and the business context so readers can get-up and running fast Explains the theoretical concepts and provides hands-on instruction on how to build and implement a data warehouseDemystifies Data Vault Modeling with beginning, intermediate, and advanced techniquesDiscusses the advantages of the data vault approach over other techniques, also including the latest updates to Data Vault 2.0 and multiple improvements to Data Vault 1.0

Scalable Big Data Architecture

A practitioners guide to choosing relevant Big Data architecture
Author: Bahaaldine Azarmi
Publisher: Apress
ISBN: 1484213262
Category: Computers
Page: 141
View: 4433

Continue Reading →

This book highlights the different types of data architecture and illustrates the many possibilities hidden behind the term "Big Data", from the usage of No-SQL databases to the deployment of stream analytics architecture, machine learning, and governance. Scalable Big Data Architecture covers real-world, concrete industry use cases that leverage complex distributed applications , which involve web applications, RESTful API, and high throughput of large amount of data stored in highly scalable No-SQL data stores such as Couchbase and Elasticsearch. This book demonstrates how data processing can be done at scale from the usage of NoSQL datastores to the combination of Big Data distribution. When the data processing is too complex and involves different processing topology like long running jobs, stream processing, multiple data sources correlation, and machine learning, it’s often necessary to delegate the load to Hadoop or Spark and use the No-SQL to serve processed data in real time. This book shows you how to choose a relevant combination of big data technologies available within the Hadoop ecosystem. It focuses on processing long jobs, architecture, stream data patterns, log analysis, and real time analytics. Every pattern is illustrated with practical examples, which use the different open sourceprojects such as Logstash, Spark, Kafka, and so on. Traditional data infrastructures are built for digesting and rendering data synthesis and analytics from large amount of data. This book helps you to understand why you should consider using machine learning algorithms early on in the project, before being overwhelmed by constraints imposed by dealing with the high throughput of Big data. Scalable Big Data Architecture is for developers, data architects, and data scientists looking for a better understanding of how to choose the most relevant pattern for a Big Data project and which tools to integrate into that pattern.

DW 2.0: The Architecture for the Next Generation of Data Warehousing


Author: W.H. Inmon,Derek Strauss,Genia Neushloss
Publisher: Elsevier
ISBN: 9780080558332
Category: Computers
Page: 400
View: 6606

Continue Reading →

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 building or using a data warehouse to see what lies ahead and 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. This book gives experienced data warehouse professionals everything they need in order to implement the new generation DW 2.0. It is designed for professionals in the IT organization, including data architects, DBAs, systems design and development professionals, as well as data warehouse and knowledge management professionals. * First book on the new generation of data warehouse architecture, DW 2.0. * Written by the "father of the data warehouse", Bill Inmon, a columnist and newsletter editor of The Bill Inmon Channel on the Business Intelligence Network. * Long overdue comprehensive coverage of the implementation of technology and tools that enable the new generation of the DW: metadata, temporal data, ETL, unstructured data, and data quality control.

Data Architecture

From Zen to Reality
Author: Charles Tupper
Publisher: Elsevier
ISBN: 9780123851277
Category: Computers
Page: 448
View: 2817

Continue Reading →

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 warehouse design. This text is a core resource for anyone customizing or aligning data management systems, taking the Zen-like idea of data architecture to an attainable reality. The book presents fundamental concepts of enterprise architecture with definitions and real-world applications and scenarios. It teaches data managers and planners about the challenges of building a data architecture roadmap, structuring the right team, and building a long term set of solutions. It includes the detail needed to illustrate how the fundamental principles are used in current business practice. The book is divided into five sections, one of which addresses the software-application development process, defining tools, techniques, and methods that ensure repeatable results. Data Architecture is intended for people in business management involved with corporate data issues and information technology decisions, ranging from data architects to IT consultants, IT auditors, and data administrators. It is also an ideal reference tool for those in a higher-level education process involved in data or information technology management. Presents fundamental concepts of enterprise architecture with definitions and real-world applications and scenarios Teaches data managers and planners about the challenges of building a data architecture roadmap, structuring the right team, and building a long term set of solutions Includes the detail needed to illustrate how the fundamental principles are used in current business practice

Modeling the Agile Data Warehouse with Data Vault


Author: Hans Hultgren
Publisher: N.A
ISBN: 9780615723082
Category: Data warehousing
Page: 434
View: 7455

Continue Reading →

Data Modeling for Agile Data Warehouse using Data Vault Modeling Approach. Includes Enterprise Data Warehouse Architecture. This is a complete guide to the data vault data modeling approach. The book also includes business and program considerations for the agile data warehousing and business intelligence program. There are over 200 diagrams and figures concerning modeling, core business concepts, architecture, business alignment, semantics, and modeling comparisons with 3NF and Dimensional modeling.

Data Lake Architecture

Designing the Data Lake and Avoiding the Garbage Dump
Author: Bill Inmon
Publisher: Technics Publications
ISBN: 1634621190
Category: Computers
Page: 166
View: 2721

Continue Reading →

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 business opportunities. Learn how to structure data lakes as well as analog, application, and text-based data ponds to provide maximum business value. Understand the role of the raw data pond and when to use an archival data pond. Leverage the four key ingredients for data lake success: metadata, integration mapping, context, and metaprocess. Bill Inmon opened our eyes to the architecture and benefits of a data warehouse, and now he takes us to the next level of data lake architecture.

Modern Data Strategy


Author: Mike Fleckenstein,Lorraine Fellows
Publisher: Springer
ISBN: 3319689932
Category: Computers
Page: 263
View: 656

Continue Reading →

This book contains practical steps business users can take to implement data management in a number of ways, including data governance, data architecture, master data management, business intelligence, and others. It defines data strategy, and covers chapters that illustrate how to align a data strategy with the business strategy, a discussion on valuing data as an asset, the evolution of data management, and who should oversee a data strategy. This provides the user with a good understanding of what a data strategy is and its limits. Critical to a data strategy is the incorporation of one or more data management domains. Chapters on key data management domains—data governance, data architecture, master data management and analytics, offer the user a practical approach to data management execution within a data strategy. The intent is to enable the user to identify how execution on one or more data management domains can help solve business issues. This book is intended for business users who work with data, who need to manage one or more aspects of the organization’s data, and who want to foster an integrated approach for how enterprise data is managed. This book is also an excellent reference for students studying computer science and business management or simply for someone who has been tasked with starting or improving existing data management.

Practical Data Science

A Guide to Building the Technology Stack for Turning Data Lakes into Business Assets
Author: Andreas François Vermeulen
Publisher: Apress
ISBN: 148423054X
Category: Computers
Page: 805
View: 2211

Continue Reading →

Learn how to build a data science technology stack and perform good data science with repeatable methods. You will learn how to turn data lakes into business assets. The data science technology stack demonstrated in Practical Data Science is built from components in general use in the industry. Data scientist Andreas Vermeulen demonstrates in detail how to build and provision a technology stack to yield repeatable results. He shows you how to apply practical methods to extract actionable business knowledge from data lakes consisting of data from a polyglot of data types and dimensions. What You'll Learn Become fluent in the essential concepts and terminology of data science and data engineering Build and use a technology stack that meets industry criteria Master the methods for retrieving actionable business knowledge Coordinate the handling of polyglot data types in a data lake for repeatable results Who This Book Is For Data scientists and data engineers who are required to convert data from a data lake into actionable knowledge for their business, and students who aspire to be data scientists and data engineers

Data Virtualization for Business Intelligence Systems

Revolutionizing Data Integration for Data Warehouses
Author: Rick F. van der Lans
Publisher: Elsevier
ISBN: 0123944252
Category: Computers
Page: 275
View: 9774

Continue Reading →

Annotation In this book, Rick van der Lans explains how data virtualization servers work, what techniques to use to optimize access to various data sources and how these products can be applied in different projects.

Agile Data Warehouse Design

Collaborative Dimensional Modeling, from Whiteboard to Star Schema
Author: Lawrence Corr,Jim Stagnitto
Publisher: DecisionOne Consulting
ISBN: 0956817203
Category: Business & Economics
Page: 304
View: 9096

Continue Reading →

Agile Data Warehouse Design is a step-by-step guide for capturing data warehousing/business intelligence (DW/BI) requirements and turning them into high performance dimensional models in the most direct way: by modelstorming (data modeling ] brainstorming) with BI stakeholders. This book describes BEAM, an agile approach to dimensional modeling, for improving communication between data warehouse designers, BI stakeholders and the whole DW/BI development team. BEAM provides tools and techniques that will encourage DW/BI designers and developers to move away from their keyboards and entity relationship based tools and model interactively with their colleagues. The result is everyone thinks dimensionally from the outset! Developers understand how to efficiently implement dimensional modeling solutions. Business stakeholders feel ownership of the data warehouse they have created, and can already imagine how they will use it to answer their business questions. Within this book, you will learn: Agile dimensional modeling using Business Event Analysis & Modeling (BEAM ) Modelstorming: data modeling that is quicker, more inclusive, more productive, and frankly more fun! Telling dimensional data stories using the 7Ws (who, what, when, where, how many, why and how) Modeling by example not abstraction; using data story themes, not crow's feet, to describe detail Storyboarding the data warehouse to discover conformed dimensions and plan iterative development Visual modeling: sketching timelines, charts and grids to model complex process measurement - simply Agile design documentation: enhancing star schemas with BEAM dimensional shorthand notation Solving difficult DW/BI performance and usability problems with proven dimensional design patterns LawrenceCorr is a data warehouse designer and educator. As Principal of DecisionOne Consulting, he helps clients to review and simplify their data warehouse designs, and advises vendors on visual data modeling techniques. He regularly teaches agile dimensional modeling courses worldwide and has taught dimensional DW/BI skills to thousands of students. Jim Stagnitto is a data warehouse and master data management architect specializing in the healthcare, financial services, and information service industries. He is the founder of the data warehousing and data mining consulting firm Llumino.

The Data Model Toolkit

Simple Skills To Model The Real World
Author: Dave Knifton
Publisher: Paragon Publishing
ISBN: 1782224734
Category: Computers
Page: 348
View: 9635

Continue Reading →

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 out a core Data Migration, developing a Data Warehouse, enhancing Data Governance processes, or even just trying to get everyone to agree on their Product specifications! This book will provide you with the skills required to start to answer these and many similar types of questions. It is not written with a focus on IT development, so you don’t need a technical background to get the most from it. But for any professional working in an organisation’s data landscape, this book will provide the skills they need to define high quality and beneficial data models quickly and easily. It does this using a wealth of practical examples, tips and techniques, as well as providing checklists and templates. It is structured into three parts: The Foundations: What are the solid foundations necessary for building effective data models? The Tools: What Tools are required to enable you to specify clear, precise and accurate data model definitions? The Deliverables: What processes will you need to successfully define the models, what will they deliver, and how can we make them beneficial to the organisation? “In this data-rich era, it is even more critical for organisations to answer the question of what their data means and the value it can bring. Those who can, will gain a competitive advantage through their use of data to streamline their operations and energise their strategies. Core to revealing this meaning, is the data model that is now, more than ever, the lynchpin of success. The Data Model Toolkit provides the essential knowledge and skills that will ensure this success.” – Reem Zahran, Global IT Platform Director, TNS “We work with many enterprise customers to help them transform their technology and it always starts with data. The key is a clear definition of their data quality, completeness and governance. This book shows you step by step how to define and use Data Models as powerful tools to define an organisation’s data and maximise its business benefit.” – John Casserly, CEO, Xceed Group

Microsoft Azure

Planning, Deploying, and Managing Your Data Center in the Cloud
Author: Marshall Copeland,Julian Soh,Anthony Puca,Mike Manning,David Gollob
Publisher: Apress
ISBN: 1484210433
Category: Computers
Page: 426
View: 8532

Continue Reading →

Written for IT and business professionals, this book provides the technical and business insight needed to plan, deploy and manage the services provided by the Microsoft Azure cloud. Find out how to integrate the infrastructure-as-a-service (IaaS) and platform-as-a-service (PaaS) models with your existing business infrastructure while maximizing availability, ensuring continuity and safety of your data, and keeping costs to a minimum. The book starts with an introduction to Microsoft Azure and how it differs from Office 365—Microsoft’s ‘other’ cloud. You'll also get a useful overview of the services available. Part II then takes you through setting up your Azure account, and gets you up-and-running on some of the core Azure services, including creating web sites and virtual machines, and choosing between fully cloud-based and hybrid storage solutions, depending on your needs. Part III now takes an in-depth look at how to integrate Azure with your existing infrastructure. The authors, Anthony Puca, Mike Manning, Brent Rush, Marshall Copeland and Julian Soh, bring their depth of experience in cloud technology and customer support to guide you through the whole process, through each layer of your infrastructure from networking to operations. High availability and disaster recovery are the topics on everyone’s minds when considering a move to the cloud, and this book provides key insights and step-by-step guidance to help you set up and manage your resources correctly to optimize for these scenarios. You’ll also get expert advice on migrating your existing VMs to Azure using InMage, mail-in and the best 3rd party tools available, helping you ensure continuity of service with minimum disruption to the business. In the book’s final chapters, you’ll find cutting edge examples of cloud technology in action, from machine learning to business intelligence, for a taste of some exciting ways your business could benefit from your new Microsoft Azure deployment.

Computational Actuarial Science with R


Author: Arthur Charpentier
Publisher: CRC Press
ISBN: 1466592591
Category: Business & Economics
Page: 656
View: 623

Continue Reading →

A Hands-On Approach to Understanding and Using Actuarial Models Computational Actuarial Science with R provides an introduction to the computational aspects of actuarial science. Using simple R code, the book helps you understand the algorithms involved in actuarial computations. It also covers more advanced topics, such as parallel computing and C/C++ embedded codes. After an introduction to the R language, the book is divided into four parts. The first one addresses methodology and statistical modeling issues. The second part discusses the computational facets of life insurance, including life contingencies calculations and prospective life tables. Focusing on finance from an actuarial perspective, the next part presents techniques for modeling stock prices, nonlinear time series, yield curves, interest rates, and portfolio optimization. The last part explains how to use R to deal with computational issues of nonlife insurance. Taking a do-it-yourself approach to understanding algorithms, this book demystifies the computational aspects of actuarial science. It shows that even complex computations can usually be done without too much trouble. Datasets used in the text are available in an R package (CASdatasets).

Microsoft Big Data Solutions


Author: Adam Jorgensen,James Rowland-Jones,John Welch,Dan Clark,Christopher Price,Brian Mitchell
Publisher: John Wiley & Sons
ISBN: 1118729080
Category: Computers
Page: 408
View: 8675

Continue Reading →

Explains how to use HDInsight along with HortonWorks Data Platform for Windows to store, manage, analyze, and share Big Data throughout the enterprise. Original.

Big Data Architect’s Handbook

A guide to building proficiency in tools and systems used by leading big data experts
Author: Syed Muhammad Fahad Akhtar
Publisher: Packt Publishing Ltd
ISBN: 1788836383
Category: Computers
Page: 486
View: 9920

Continue Reading →

A comprehensive end-to-end guide that gives hands-on practice in big data and Artificial Intelligence Key Features Learn to build and run a big data application with sample code Explore examples to implement activities that a big data architect performs Use Machine Learning and AI for structured and unstructured data Book Description The big data architects are the “masters” of data, and hold high value in today’s market. Handling big data, be it of good or bad quality, is not an easy task. The prime job for any big data architect is to build an end-to-end big data solution that integrates data from different sources and analyzes it to find useful, hidden insights. Big Data Architect’s Handbook takes you through developing a complete, end-to-end big data pipeline, which will lay the foundation for you and provide the necessary knowledge required to be an architect in big data. Right from understanding the design considerations to implementing a solid, efficient, and scalable data pipeline, this book walks you through all the essential aspects of big data. It also gives you an overview of how you can leverage the power of various big data tools such as Apache Hadoop and ElasticSearch in order to bring them together and build an efficient big data solution. By the end of this book, you will be able to build your own design system which integrates, maintains, visualizes, and monitors your data. In addition, you will have a smooth design flow in each process, putting insights in action. What you will learn Learn Hadoop Ecosystem and Apache projects Understand, compare NoSQL database and essential software architecture Cloud infrastructure design considerations for big data Explore application scenario of big data tools for daily activities Learn to analyze and visualize results to uncover valuable insights Build and run a big data application with sample code from end to end Apply Machine Learning and AI to perform big data intelligence Practice the daily activities performed by big data architects Who this book is for Big Data Architect’s Handbook is for you if you are an aspiring data professional, developer, or IT enthusiast who aims to be an all-round architect in big data. This book is your one-stop solution to enhance your knowledge and carry out easy to complex activities required to become a big data architect.

The Language of Architecture

26 Principles Every Architect Should Know
Author: Andrea Simitch,Val Warke
Publisher: Rockport Publishers Incorporated
ISBN: 1592538584
Category: Architecture
Page: 224
View: 6678

Continue Reading →

DIVLearning a new discipline is similar to learning a new language; in order to master the foundation of architecture, you must first master the basic building blocks of its language – the definitions, function, and usage. Language of Architecture provides students and professional architects with the basic elements of architectural design, divided into twenty-six easy-to-comprehend chapters. This visual reference includes an introductory, historical view of the elements, as well as an overview of how these elements can and have been used across multiple design disciplines./divDIV /divDIVWhether you’re new to the field or have been an architect for years, you’ll want to flip through the pages of this book throughout your career and use it as the go-to reference for inspiration, ideas, and reminders of how a strong knowledge of the basics allows for meaningful, memorable, and beautiful fashions that extend beyond trends./divDIV /divDIVThis comprehensive learning tool is the one book you’ll want as a staple in your library./divDIV /div

Data-Driven Security

Analysis, Visualization and Dashboards
Author: Jay Jacobs,Bob Rudis
Publisher: John Wiley & Sons
ISBN: 1118793722
Category: Computers
Page: 352
View: 6961

Continue Reading →

Uncover hidden patterns of data and respond with countermeasures Security professionals need all the tools at their disposal to increase their visibility in order to prevent security breaches and attacks. This careful guide explores two of the most powerful ? data analysis and visualization. You'll soon understand how to harness and wield data, from collection and storage to management and analysis as well as visualization and presentation. Using a hands-on approach with real-world examples, this book shows you how to gather feedback, measure the effectiveness of your security methods, and make better decisions. Everything in this book will have practical application for information security professionals. Helps IT and security professionals understand and use data, so they can thwart attacks and understand and visualize vulnerabilities in their networks Includes more than a dozen real-world examples and hands-on exercises that demonstrate how to analyze security data and intelligence and translate that information into visualizations that make plain how to prevent attacks Covers topics such as how to acquire and prepare security data, use simple statistical methods to detect malware, predict rogue behavior, correlate security events, and more Written by a team of well-known experts in the field of security and data analysis Lock down your networks, prevent hacks, and thwart malware by improving visibility into the environment, all through the power of data and Security Using Data Analysis, Visualization, and Dashboards.

Ulysses


Author: James Joyce,General Press
Publisher: GENERAL PRESS
ISBN: 8180320995
Category: Fiction
Page: 860
View: 5082

Continue Reading →

'Ulysses' is a novel by Irish writer James Joyce. It was first serialised in parts in the American journal 'The Little Review' from March 1918 to December 1920, and then published in its entirety by Sylvia Beach in February 1922, in Paris. 'Ulysses' has survived bowdlerization, legal action and bitter controversy. Capturing a single day in the life of Dubliner Leopold Bloom, his friends Buck Mulligan and Stephen Dedalus, his wife Molly, and a scintillating cast of supporting characters, Joyce pushes Celtic lyricism and vulgarity to splendid extremes. An undisputed modernist classic, its ceaseless verbal inventiveness and astonishingly wide-ranging allusions confirm its standing as an imperishable monument to the human condition. It takes readers into the inner realms of human consciousness using the interior monologue style that came to be called stream of consciousness. In addition to this psychological characteristic, it gives a realistic portrait of the life of ordinary people living in Dublin, Ireland, on June 16, 1904. The novel was the subject of a famous obscenity trial in 1933, but was found by a U.S. district court in New York to be a work of art. The furor over the novel made Joyce a celebrity. In the long run, the work placed him at the forefront of the modern period of the early 1900s when literary works, primarily in the first two decades, explored interior lives and subjective reality in a new idiom, attempting to probe the human psyche in order to understand the human condition. This richly-allusive novel, revolutionary in its modernistic experimentalism, was hailed as a work of genius by W.B. Yeats, T.S. Eliot and Ernest Hemingway. Scandalously frank, wittily erudite, mercurially eloquent, resourcefully comic and generously humane, 'Ulysses' offers the reader a life-changing experience. Publisher : General Press