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Sprache: Englisch
Verlag: O'Reilly Media 10/11/2022, 2022
ISBN 10: 1098112040 ISBN 13: 9781098112042
Anbieter: BargainBookStores, Grand Rapids, MI, USA
Paperback or Softback. Zustand: New. Data Quality Fundamentals: A Practitioner's Guide to Building Trustworthy Data Pipelines. Book.
Paperback. Zustand: New. 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 pipelinesWrite scripts to make data checks and identify broken pipelines with data observabilityProgram your own data quality monitors from scratchDevelop and lead data quality initiatives at your companyGenerate a dashboard to highlight your company's key data assetsAutomate data lineage graphs across your data ecosystemBuild anomaly detectors for your critical data assets.
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In den WarenkorbPaperback. Zustand: Fine.
Sprache: Englisch
Verlag: O'Reilly Media, Sebastopol, 2022
ISBN 10: 1098112040 ISBN 13: 9781098112042
Anbieter: Grand Eagle Retail, Bensenville, IL, USA
Paperback. Zustand: new. Paperback. 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 pipelinesWrite scripts to make data checks and identify broken pipelines with data observabilityProgram your own data quality monitors from scratchDevelop and lead data quality initiatives at your companyGenerate a dashboard to highlight your company's key data assetsAutomate data lineage graphs across your data ecosystemBuild anomaly detectors for your critical data assets 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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In den WarenkorbPaperback. Zustand: New. 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 pipelinesWrite scripts to make data checks and identify broken pipelines with data observabilityProgram your own data quality monitors from scratchDevelop and lead data quality initiatives at your companyGenerate a dashboard to highlight your company's key data assetsAutomate data lineage graphs across your data ecosystemBuild anomaly detectors for your critical data assets.
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Sprache: Englisch
Verlag: O'Reilly Media 2022-09-30, 2022
ISBN 10: 1098112040 ISBN 13: 9781098112042
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In den WarenkorbPaperback. Zustand: New. 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 pipelinesWrite scripts to make data checks and identify broken pipelines with data observabilityProgram your own data quality monitors from scratchDevelop and lead data quality initiatives at your companyGenerate a dashboard to highlight your company's key data assetsAutomate data lineage graphs across your data ecosystemBuild anomaly detectors for your critical data assets.
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Zustand: New. 2022. Paperback. . . . . . Books ship from the US and Ireland.
Anbieter: moluna, Greven, Deutschland
Zustand: New. Über den AutorBarr Moses is the CEO and co-founder of Monte Carlo, a data reliability company. In her decade-long career in data, Barr has served as commander of a data intelligence unit in the Israeli Air Force, a consultant at Bai.
Sprache: Englisch
Verlag: O'Reilly Media, Sebastopol, 2022
ISBN 10: 1098112040 ISBN 13: 9781098112042
Anbieter: AussieBookSeller, Truganina, VIC, Australien
Paperback. Zustand: new. Paperback. 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 pipelinesWrite scripts to make data checks and identify broken pipelines with data observabilityProgram your own data quality monitors from scratchDevelop and lead data quality initiatives at your companyGenerate a dashboard to highlight your company's key data assetsAutomate data lineage graphs across your data ecosystemBuild anomaly detectors for your critical data assets 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.