Data Quality Fundamentals: A Practitioner's Guide to Building Trustworthy Data Pipelines - Brossura

Moses, Barr; Gavish, Lior; Vorwerck, Molly

Brossura

ISBN 10: 1098112040 ISBN 13: 9781098112042

Casa editrice: O'Reilly Media, 2022

Riassunto

Do your product dashboards look funky? Are your quarterly reports stale? Is the data set you're using broken or just plain wrong? These problems affect almost every team, yet they're usually addressed on an ad hoc basis and in a reactive manner. If you answered yes to these questions, this book is for you.

Many data engineering teams today face the "good pipelines, bad data" problem. It doesn't matter how advanced your data infrastructure is if the data you're piping is bad. In this book, Barr Moses, Lior Gavish, and Molly Vorwerck, from the data observability company Monte Carlo, explain how to tackle data quality and trust at scale by leveraging best practices and technologies used by some of the world's most innovative companies.

• Build more trustworthy and reliable data pipelines
• Write scripts to make data checks and identify broken pipelines with data observability
• Learn how to set and maintain data SLAs, SLIs, and SLOs
• Develop and lead data quality initiatives at your company
• Learn how to treat data services and systems with the diligence of production software
• Automate data lineage graphs across your data ecosystem
• Build anomaly detectors for your critical data assets

Su questo libro

Informazioni sull?autore

Do your product dashboards look funky? Are your quarterly reports stale? Is the dataset you're using broken or just plain wrong? These problems affect almost every team, yet they're usually addressed on an ad hoc basis and in a reactive manner. If you answered yes to any of the questions above, this book is for you.

Many data engineering teams today face the "good pipelines, bad data" problem. It doesn't matter how advanced your data infrastructure is if the data you're piping is bad. In this book, Barr Moses, Lior Gavish, and Molly Vorwerck from the data reliability company Monte Carlo explain how to tackle data quality and trust at scale by leveraging best practices and technologies used by some of the world's most innovative companies.

• Build more trustworthy and reliable data pipelines
• Write scripts to make data checks and identify broken pipelines with data observability
• Program your own data quality monitors from scratch
• Develop and lead data quality initiatives at your company
• Generate a dashboard to highlight your company's key data assets
• Automate data lineage graphs across your data ecosystem
• Build anomaly detectors for your critical data assets

Dettagli dell'edizione

• Editore: O'Reilly Media
• Data di pubblicazione: 2022
• Lingua: Inglese
• ISBN 10: 1098112040
• ISBN 13: 9781098112042
• Rilegatura: Copertina flessibile
• Numero edizione: 1
• Numero di pagine: 288
• Contatto del produttore: O'Reilly; https://oreilly.com; ; 1005 Gravenstein Highway North; Sebastopol; CA; 95472; U.S.A.
• Persona responsabile: non disponibile