DOWNLOAD/PDF Foundations for Architecting Data Solutions: Managing Successful Data Projects
COPY LINK: https://pdf.bookcenterapp.com/yumpu/1492038741 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 collect
COPY LINK: https://pdf.bookcenterapp.com/yumpu/1492038741
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 collect
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Foundations for Architecting Data
Solutions: Managing Successful Data
Projects
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Foundations for Architecting Data Solutions:
Managing Successful Data Projects
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Foundations for Architecting Data Solutions:
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COPY LINK: https://pdf.bookcenterapp.com/yumpu/1492038741 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 collect
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