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Rebuilding reliable data pipelines through modern tools /
When data-driven applications fail, identifying the cause is both challenging and time-consuming-especially as data pipelines become more and more complex. Hunting for the root cause of application failure from messy, raw, and distributed logs is difficult for performance experts and a nightmare for...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Sebastopol, CA :
O'Reilly Media,
[2019]
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| Edición: | First edition. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Malaska, Ted, |e author. | |
| 245 | 1 | 0 | |a Rebuilding reliable data pipelines through modern tools / |c Ted Malaska ; with the assistance of Shivnath Babu. |
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| 588 | 0 | |a Online resource; title from title page (Safari, viewed August 12, 2019). | |
| 520 | |a When data-driven applications fail, identifying the cause is both challenging and time-consuming-especially as data pipelines become more and more complex. Hunting for the root cause of application failure from messy, raw, and distributed logs is difficult for performance experts and a nightmare for data operations teams. This report examines DataOps processes and tools that enable you to manage modern data pipelines efficiently. Author Ted Malaska describes a data operations framework and shows you the importance of testing and monitoring to plan, rebuild, automate, and then manage robust data pipelines-whether it's in the cloud, on premises, or in a hybrid configuration. You'll also learn ways to apply performance monitoring software and AI to your data pipelines in order to keep your applications running reliably. You'll learn: How performance management software can reduce the risk of running modern data applications Methods for applying AI to provide insights, recommendations, and automation to operationalize big data systems and data applications How to plan, migrate, and operate big data workloads and data pipelines in the cloud and in hybrid deployment models. | ||
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| 650 | 6 | |a Temps réel (Informatique) | |
| 650 | 6 | |a Exploration de données (Informatique) | |
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