| Description |
1 online resource (1 volume) : illustrations |
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text file |
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PDF |
| Series |
The expert's voice in big data |
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Expert's voice in big data.
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| Contents |
Big data introduction -- Big data application architecture -- Big data ingestion and streaming patterns -- Big data storage patterns -- Big data access patterns -- Data discovery and analysis patterns -- Big data visualization patterns -- Big data deployment patterns -- Big data NFRs -- Big data case studies -- Resources, references, and tools. |
| Bibliography |
Includes bibliographical references and index. |
| Summary |
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. |
| Language |
English. |
| Subject |
Big data.
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Software architecture.
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Data mining.
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Data Mining |
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Données volumineuses. |
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Architecture logicielle. |
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Exploration de données (Informatique) |
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Big data |
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Data mining |
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Software architecture |
| Added Author |
Shah, Himanshu (Software engineer)
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| Added Title |
Big data application architecture questions and answers |
| In: |
Springer eBooks |
| Other Form: |
Print version: 9781430262923 |
| ISBN |
9781430262930 (electronic bk.) |
|
1430262931 (electronic bk.) |
| Standard No. |
10.1007/978-1-4302-6293-0 doi |
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