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Foundations for Architecting Data Solutions: Managing Successful Data Projects

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Description:While many companies ponder implementation details such as distributed processing engines and algorithms for data analysis, this practical book takes a much wider view of big data development, starting with initial planning and moving diligently toward execution. Authors Ted Malaska and Jonathan Seidman guide you through the major components necessary to start, architect, and develop successful big data projects.Everyone from CIOs and COOs to lead architects and developers will explore a variety of big data architectures and applications, from massive data pipelines to web-scale applications. Each chapter addresses a piece of the software development life cycle and identifies patterns to maximize long-term success throughout the life of your project.Start the planning process by considering the key data project typesUse guidelines to evaluate and select data management solutionsReduce risk related to technology, your team, and vague requirementsExplore system interface design using APIs, REST, and pub/sub systemsChoose the right distributed storage system for your big data systemPlan and implement metadata collections for your data architectureUse data pipelines to ensure data integrity from source to final storageEvaluate the attributes of various engines for processing the data you collectWe have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Foundations for Architecting Data Solutions: Managing Successful Data Projects. To get started finding Foundations for Architecting Data Solutions: Managing Successful Data Projects, you are right to find our website which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of different products represented.

Pages: 190

Format: PDF, EPUB & Kindle Edition

Publisher: O'Reilly Media

Release: 2018

ISBN: 1492038695

Description: While many companies ponder implementation details such as distributed processing engines and algorithms for data analysis, this practical book takes a much wider view of big data development, starting with initial planning and moving diligently toward execution. Authors Ted Malaska and Jonathan Seidman guide you through the major components necessary to start, architect, and develop successful big data projects.Everyone from CIOs and COOs to lead architects and developers will explore a variety of big data architectures and applications, from massive data pipelines to web-scale applications. Each chapter addresses a piece of the software development life cycle and identifies patterns to maximize long-term success throughout the life of your project.Start the planning process by considering the key data project typesUse guidelines to evaluate and select data management solutionsReduce risk related to technology, your team, and vague requirementsExplore system interface design using APIs, REST, and pub/sub systemsChoose the right distributed storage system for your big data systemPlan and implement metadata collections for your data architectureUse data pipelines to ensure data integrity from source to final storageEvaluate the attributes of various engines for processing the data you collectWe have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Foundations for Architecting Data Solutions: Managing Successful Data Projects. To get started finding Foundations for Architecting Data Solutions: Managing Successful Data Projects, you are right to find our website which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of different products represented.

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Foundations for Architecting Data Solutions: Managing Successful Data Projects

Foundations for Architecting Data Solutions: Managing Successful Data Projects

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