Name: Foundations for Architecting Data Solutions: Managing Successful Data Projects (en Inglés)
Price: 50.40 EUR
Availability: InStock
Author: Malaska, Ted ; Seidman, Jonathan
ISBN: 9781492038740

portada Foundations for Architecting Data Solutions: Managing Successful Data Projects (en Inglés)

Formato

Libro Físico

Autor

Ted Malaska

Jonathan Seidman

Editorial

O'Reilly Media

Año

2018

Idioma

Inglés

N° páginas

190

Encuadernación

Tapa Blanda

Dimensiones

23.1 x 17.8 x 1.0 cm

Peso

0.32 kg.

ISBN13

9781492038740

N° edición

Categorías

Data warehousing
Data mining: minería de datos
Sistemas distribuidos

Foundations for Architecting Data Solutions: Managing Successful Data Projects (en Inglés)

Ted Malaska (Autor) · Jonathan Seidman (Autor) · O'Reilly Media · Tapa Blanda

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Origen: Reino Unido (Costos de importación incluídos en el precio)

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Reseña del libro "Foundations for Architecting Data Solutions: Managing Successful Data Projects (en Inglés)"

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 types Use guidelines to evaluate and select data management solutions Reduce risk related to technology, your team, and vague requirements Explore system interface design using APIs, REST, and pub/sub systems Choose the right distributed storage system for your big data system Plan and implement metadata collections for your data architecture Use data pipelines to ensure data integrity from source to final storage Evaluate the attributes of various engines for processing the data you collect