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Architecting Data-Intensive SaaS Applications
Through explosive growth in the past decade, data now drives significant portions of our lives, from crowdsourced restaurant recommendations to AI systems identifying effective medical treatments. Software developers have unprecedented opportunity to build data applications that generate value from...
| Autores principales: | , , , , |
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| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
[Erscheinungsort nicht ermittelbar]
O'Reilly Media, Inc.
2021
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| Edición: | 1st edition. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 520 | |a Through explosive growth in the past decade, data now drives significant portions of our lives, from crowdsourced restaurant recommendations to AI systems identifying effective medical treatments. Software developers have unprecedented opportunity to build data applications that generate value from massive datasets across use cases such as customer 360, application health and security analytics, the IoT, machine learning, and embedded analytics. With this report, product managers, architects, and engineering teams will learn how to make key technical decisions when building data-intensive applications, including how to implement extensible data pipelines and share data securely. The report includes design considerations for making these decisions and uses the Snowflake Data Cloud to illustrate best practices. This report explores: Why data applications matter: Get an introduction to data applications and some of the most common use cases Evaluating platforms for building data apps: Evaluate modern data platforms to confidently consider the merits of potential solutions Building scalable data applications: Learn design patterns and best practices for storage, compute, and security Handling and processing data: Explore techniques and real-world examples for building data pipelines to support data applications Designing for data sharing: Learn best practices for sharing data in modern data applications. | ||
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