Fundamentals of data observability: implement trustworthy end-to-end data solutions
Quickly detect, troubleshoot, and prevent a wide range of data issues through data observability, a set of best practices that enables data teams to gain greater visibility of data and its usage. If you're a data engineer, data architect, or machine learning engineer who depends on the quality...
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| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Sebastopol, CA
O'Reilly Media, Inc.
2023
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| Edition: | First edition. |
| Subjects: | |
| Links: | https://learning.oreilly.com/library/view/-/9781098133283/?ar |
| Summary: | Quickly detect, troubleshoot, and prevent a wide range of data issues through data observability, a set of best practices that enables data teams to gain greater visibility of data and its usage. If you're a data engineer, data architect, or machine learning engineer who depends on the quality of your data, this book shows you how to focus on the practical aspects of introducing data observability in your everyday work. Author Andy Petrella helps you build the right habits to identify and solve data issues, such as data drifts and poor quality, so you can stop their propagation in data applications, pipelines, and analytics. You'll learn ways to introduce data observability, including setting up a framework for generating and collecting all the information you need. |
| Item Description: | Includes index |
| Physical Description: | 1 Online-Ressource (264 Seiten) illustrations |
| ISBN: | 9781098133269 1098133269 |
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| spelling | Petrella, Andy VerfasserIn aut Fundamentals of data observability implement trustworthy end-to-end data solutions Andy Petrella First edition. Sebastopol, CA O'Reilly Media, Inc. 2023 1 Online-Ressource (264 Seiten) illustrations Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Includes index Quickly detect, troubleshoot, and prevent a wide range of data issues through data observability, a set of best practices that enables data teams to gain greater visibility of data and its usage. If you're a data engineer, data architect, or machine learning engineer who depends on the quality of your data, this book shows you how to focus on the practical aspects of introducing data observability in your everyday work. Author Andy Petrella helps you build the right habits to identify and solve data issues, such as data drifts and poor quality, so you can stop their propagation in data applications, pipelines, and analytics. You'll learn ways to introduce data observability, including setting up a framework for generating and collecting all the information you need. Electronic data processing Big data Données volumineuses 1098133293 Erscheint auch als Druck-Ausgabe 1098133293 |
| spellingShingle | Petrella, Andy Fundamentals of data observability implement trustworthy end-to-end data solutions Electronic data processing Big data Données volumineuses |
| title | Fundamentals of data observability implement trustworthy end-to-end data solutions |
| title_auth | Fundamentals of data observability implement trustworthy end-to-end data solutions |
| title_exact_search | Fundamentals of data observability implement trustworthy end-to-end data solutions |
| title_full | Fundamentals of data observability implement trustworthy end-to-end data solutions Andy Petrella |
| title_fullStr | Fundamentals of data observability implement trustworthy end-to-end data solutions Andy Petrella |
| title_full_unstemmed | Fundamentals of data observability implement trustworthy end-to-end data solutions Andy Petrella |
| title_short | Fundamentals of data observability |
| title_sort | fundamentals of data observability implement trustworthy end to end data solutions |
| title_sub | implement trustworthy end-to-end data solutions |
| topic | Electronic data processing Big data Données volumineuses |
| topic_facet | Electronic data processing Big data Données volumineuses |
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