Automating Data Quality Monitoring | Scaling Beyond Rules with Machine Learning
Idioma: inglés
Editorial: O'Reilly Media, 2024
- Tapa blanda
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Librería: preigu, Osnabrück, Alemaniapreigu
Vendedor de IberLibro desde 5 de agosto de 2024
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Automating Data Quality Monitoring | Scaling Beyond Rules with Machine Learning | Jeremy Stanley (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2024 | O'Reilly Media | EAN 9781098145934 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.
N° de ref. del artículo 127140748
- Título
- Automating Data Quality Monitoring | Scaling Beyond Rules with Machine Learning
- Autor
- Jeremy Stanley (u. a.)
- Editorial
- O'Reilly Media
- Año de publicación
- 2024
- Estado
- Neu
- Encuadernación
- Taschenbuch
- Idioma
- inglés
- ISBN 10
- 1098145933
- ISBN 13
- 9781098145934
- Peso del artículo
- 396 gramos
- Dimensiones
- 230 x 174 x 14 mm
- Catálogos de vendedores
- Bücher
The world's businesses ingest a combined 2.5 quintillion bytes of data every day. But how much of this vast amount of data--used to build products, power AI systems, and drive business decisions--is poor quality or just plain bad? This practical book shows you how to ensure that the data your organization relies on contains only high-quality records.
Most data engineers, data analysts, and data scientists genuinely care about data quality, but they often don't have the time, resources, or understanding to create a data quality monitoring solution that succeeds at scale. In this book, Jeremy Stanley and Paige Schwartz from Anomalo explain how you can use automated data quality monitoring to cover all your tables efficiently, proactively alert on every category of issue, and resolve problems immediately.
This book will help you:
- Learn why data quality is a business imperative
- Understand and assess unsupervised learning models for detecting data issues
- Implement notifications that reduce alert fatigue and let you triage and resolve issues quickly
- Integrate automated data quality monitoring with data catalogs, orchestration layers, and BI and ML systems
- Understand the limits of automated data quality monitoring and how to overcome them
- Learn how to deploy and manage your monitoring solution at scale
- Maintain automated data quality monitoring for the long term
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preigu
Osnabrück, Alemania
Vendedor de IberLibro desde 5 de agosto de 2024
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