Bộ sách luyện thi ETS 2023 LC&RC
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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
Categories:Computers - Databases
Year:2024
Edition:1
Publisher:O'Reilly Media
Language:english
Pages:220
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