Name: Big Data Application Architecture Q&A: A Problem - Solution Approach (en Inglés)
Price: 1073.16 MXN
Availability: InStock
Author: Sawant, Nitin ; Shah, Himanshu
ISBN: 9781430262923

Envío gratis

portada Big Data Application Architecture Q&A: A Problem - Solution Approach (en Inglés)

Formato

Libro Físico

Autor

Nitin Sawant

Himanshu Shah

Editorial

Apress

Idioma

Inglés

N° páginas

172

Encuadernación

Tapa Blanda

Dimensiones

23.5 x 19.1 x 0.9 cm

Peso

0.30 kg.

ISBN13

9781430262923

Categorías

Medios de almacenamiento y dispositivos periféricos
Data warehousing
Recuperación de información
Administración del sistema
Procesamiento paralelo

Big Data Application Architecture Q&A: A Problem - Solution Approach (en Inglés)

Nitin Sawant (Autor) · Himanshu Shah (Autor) · Apress · Tapa Blanda

Libro Nuevo

$ 1,073.16

$ 2,146.31

Ahorras: $ 1,073.16

50% descuento

Envío normal

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

Se enviará desde nuestra bodega entre el Viernes 07 de Junio y el Miércoles 19 de Junio.

Lo recibirás en cualquier lugar de México entre 1 y 3 días hábiles luego del envío.

Reseña del libro "Big Data Application Architecture Q&A: A Problem - Solution Approach (en Inglés)"

Big Data Application Architecture Pattern Recipes provides an insight into heterogeneous infrastructures, databases, and visualization and analytics tools used for realizing the architectures of big data solutions. Its problem-solution approach helps in selecting the right architecture to solve the problem at hand. In the process of reading through these problems, you will learn harness the power of new big data opportunities which various enterprises use to attain real-time profits. Big Data Application Architecture Pattern Recipes answers one of the most critical questions of this time 'how do you select the best end-to-end architecture to solve your big data problem?'.The book deals with various mission critical problems encountered by solution architects, consultants, and software architects while dealing with the myriad options available for implementing a typical solution, trying to extract insight from huge volumes of data in real-time and across multiple relational and non-relational data types for clients from industries like retail, telecommunication, banking, and insurance. The patterns in this book provide the strong architectural foundation required to launch your next big data application. The architectures for realizing these opportunities are based on relatively less expensive and heterogeneous infrastructures compared to the traditional monolithic and hugely expensive options that exist currently. This book describes and evaluates the benefits of heterogeneity which brings with it multiple options of solving the same problem, evaluation of trade-offs and validation of 'fitness-for-purpose' of the solution.