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Designing Data-Intensive Applications

Designing Data-Intensive Applications
Author: Martin Kleppmann
Publisher: "O'Reilly Media, Inc."
Total Pages: 658
Release: 2017-03-16
Genre: Computers
ISBN: 1491903104
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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 by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications. Peer under the hood of the systems you already use, and learn how to use and operate them more effectively Make informed decisions by identifying the strengths and weaknesses of different tools Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity Understand the distributed systems research upon which modern databases are built Peek behind the scenes of major online services, and learn from their architectures

The TTL Data Book

The TTL Data Book
Author: Texas Instruments Incorporated
Publisher:
Total Pages: 362
Release: 1984
Genre: Diodes, Schottky-barrier
ISBN:
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Data-Oriented Design

Data-Oriented Design
Author: Richard Fabian
Publisher: Richard Fabian
Total Pages: 308
Release: 2018-09-29
Genre:
ISBN: 9781916478701
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The projects tackled by the software development industry have grown in scale and complexity. Costs are increasing along with the number of developers. Power bills for distributed projects have reached the point where optimisations pay literal dividends. Over the last 10 years, a software development movement has gained traction, a movement founded in games development. The limited resources and complexity of the software and hardware needed to ship modern game titles demanded a different approach. Data-oriented design is inspired by high-performance computing techniques, database design, and functional programming values. It provides a practical methodology that reduces complexity while improving performance of both your development team and your product. Understand the goal, understand the data, understand the hardware, develop the solution. This book presents foundations and principles helping to build a deeper understanding of data-oriented design. It provides instruction on the thought processes involved when considering data as the primary detail of any project.

Designing with Data

Designing with Data
Author: Rochelle King
Publisher: "O'Reilly Media, Inc."
Total Pages: 275
Release: 2017-03-29
Genre: Computers
ISBN: 1449334954
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On the surface, design practices and data science may not seem like obvious partners. But these disciplines actually work toward the same goal, helping designers and product managers understand users so they can craft elegant digital experiences. While data can enhance design, design can bring deeper meaning to data. This practical guide shows you how to conduct data-driven A/B testing for making design decisions on everything from small tweaks to large-scale UX concepts. Complete with real-world examples, this book shows you how to make data-driven design part of your product design workflow. Understand the relationship between data, business, and design Get a firm grounding in data, data types, and components of A/B testing Use an experimentation framework to define opportunities, formulate hypotheses, and test different options Create hypotheses that connect to key metrics and business goals Design proposed solutions for hypotheses that are most promising Interpret the results of an A/B test and determine your next move

Data-driven Organization Design

Data-driven Organization Design
Author: Rupert Morrison
Publisher: Kogan Page Publishers
Total Pages: 368
Release: 2015-10-03
Genre: Business & Economics
ISBN: 0749474424
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SHORTLISTED: CMI Management Book of the Year 2017 - Management Futures Category Data is changing the nature of competition. Making sense of it is tough; taking advantage of it is even tougher. There is a clear business opportunity for organizations to use data and analytics to transform business performance. Data-driven Organization Design provides a practical framework for HR and organization design practitioners to build a baseline of data, set objectives, carry out fixed and dynamic process design, map competencies, and right-size the organization so everyone performs to their potential and organizations have a hope of getting and sustaining a competitive edge. Data-driven Organization Design shows how to collect the right data on organizations, present it meaningfully and ask the right questions of it to help complex, fluid organizations constantly evolve and meet moving objectives. Through the use of case studies, practical tips, and sample exercises, it explains in detail how to use data and analytics to connect all the elements of the system so you can design an environment for people to perform, an organization which has the right people, in the right place, doing the right things, at the right time. Whether you are looking to implement a long-term transformation, large redesign, or a one-off small scale project, Data-driven Organization Design will guide you through making the most of organizational data and analytics to drive business performance.

Data Design

Data Design
Author: Per Mollerup
Publisher: Bloomsbury Publishing
Total Pages: 176
Release: 2015-07-16
Genre: Business & Economics
ISBN: 1408191881
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This book is a highly practical guide to the graphic representation of quantities, locations, connections and other forms of data, founded on solid design principles. 0Easy to understand, the book has been created to assist designers, researchers, and writers in expressing visually through points, lines, and areas that which by words, letters, and numbers alone often have difficulty communicating. It describes problems, principles, and solutions for the visual display of information, and presents real-world didactic examples, taken from, for example, the 'Financial Times' and the 'Wall Street Journal'. 0The book's theoretical basis is strong, but not at the expense of clarity; readers will understand both what to do and why. After a number of introductory chapters that describe what is common to all types of visual displays, the book continues with three main parts that deal with displays showing quantities, locations, and connections. 0This book an essential guide to how data can, and should be, presented in order to effortlessly communicate information with clarity, elegance and style. Highly illustrated and with beautiful simplicity, this book is an indispensable guide to the clear and effective presentation of information.

Data Mapping for Data Warehouse Design

Data Mapping for Data Warehouse Design
Author: Qamar Shahbaz
Publisher: Elsevier
Total Pages: 181
Release: 2015-12-08
Genre: Computers
ISBN: 0128053356
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Data mapping in a data warehouse is the process of creating a link between two distinct data models’ (source and target) tables/attributes. Data mapping is required at many stages of DW life-cycle to help save processor overhead; every stage has its own unique requirements and challenges. Therefore, many data warehouse professionals want to learn data mapping in order to move from an ETL (extract, transform, and load data between databases) developer to a data modeler role. Data Mapping for Data Warehouse Design provides basic and advanced knowledge about business intelligence and data warehouse concepts including real life scenarios that apply the standard techniques to projects across various domains. After reading this book, readers will understand the importance of data mapping across the data warehouse life cycle. Covers all stages of data warehousing and the role of data mapping in each Includes a data mapping strategy and techniques that can be applied to many situations Based on the author’s years of real-world experience designing solutions

Data, Matter, Design

Data, Matter, Design
Author: Frank Melendez
Publisher: Routledge
Total Pages: 338
Release: 2020-09-29
Genre: Architecture
ISBN: 1000064417
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Data, Matter, Design presents a comprehensive overview of current design processes that rely on the input of data and use of computational design strategies, and their relationship to an array of outputs. Technological changes, through the use of computational tools and processes, have radically altered and influenced our relationship to cities and the methods by which we design architecture, urban, and landscape systems. This book presents a wide range of curated projects and contributed texts by leading architects, urbanists, and designers that transform data as an abstraction, into spatial, experiential, and performative configurations within urban ecologies, emerging materials, robotic agents, adaptive fields, and virtual constructs. Richly illustrated with over 200 images, Data, Matter, Design is an essential read for students, academics, and professionals to evaluate and discuss how data in design methodologies and theoretical discourses have evolved in the last two decades and why processes of data collection, measurement, quantification, simulation, algorithmic control, and their integration into methods of reading and producing spatial conditions, are becoming vital in academic and industry practices.

Analyzing Single System Design Data

Analyzing Single System Design Data
Author: William Nugent
Publisher: Oxford University Press
Total Pages: 184
Release: 2010
Genre: Social Science
ISBN: 0195369629
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Single system, or single case, design studies are a convenient method for evaluating practice, allowing professionals to track clients' response to treatment and change over time. They also allow researchers to gather data where it might be difficult to conduct a study involving treatment and control groups; in a school setting, or a community mental health agency, for example, random assignment may be impossible, whereas individual student or client progress across time can be more easily monitored.This pocket guide reviews a wide range of techniques for analyzing single system design data, including visual analysis methods, graphical methods, and statistical methods. From basic visual observation to complex ARIMA statistical models for use with interrupted time series designs, numerous data analysis methods are described and illustrated in this unique and handy book. The author frankly describes limitations and strengths of the data analysis methods so that readers can select an appropriate method and use the results responsibly in order to improve practice and client well-being.This accessible yet in-depth introduction will serve as a highly practical resource for doctoral students and researchers alike.

The Data Science Design Manual

The Data Science Design Manual
Author: Steven S. Skiena
Publisher: Springer
Total Pages: 456
Release: 2017-07-01
Genre: Computers
ISBN: 3319554441
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This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well. Additional learning tools: Contains “War Stories,” offering perspectives on how data science applies in the real world Includes “Homework Problems,” providing a wide range of exercises and projects for self-study Provides a complete set of lecture slides and online video lectures at www.data-manual.com Provides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapter Recommends exciting “Kaggle Challenges” from the online platform Kaggle Highlights “False Starts,” revealing the subtle reasons why certain approaches fail Offers examples taken from the data science television show “The Quant Shop” (www.quant-shop.com)