Moses barr (41 Ergebnisse)

Paperback. Zustand: Good. 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.…

paperback. Zustand: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority.

paperback. Zustand: Acceptable.

Soft cover. Zustand: Very Good. No Dust Jacket. 178+pp. Photos. Map. Minnesota history. Size: 12mo - over 6?" - 7?" Tall.

Paperback. Zustand: Good. 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.…

Paperback. Zustand: As New. No Jacket. Pages are clean and are not marred by notes or folds of any kind. ~ ThriftBooks: Read More, Spend Less.

Zustand: New.

Zustand: New. Brand New! Not Overstocks or Low Quality Book Club Editions! Direct From the Publisher! We're not a giant, faceless warehouse organization! We're a small town bookstore that loves books and loves it's customers! Buy from Lakeside Books.

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.…

Paperback or Softback. Zustand: New. Data Quality Fundamentals: A Practitioner's Guide to Building Trustworthy Data Pipelines. Book.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: As New. Unread book in perfect condition.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: New.

Paperback. Zustand: New. Brand new book, sourced directly from publisher. Dispatch time is 24-48 hours from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

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.…

Zustand: very good. Gut/Very good: Buch bzw. Schutzumschlag mit wenigen Gebrauchsspuren an Einband, Schutzumschlag oder Seiten. / Describes a book or dust jacket that does show some signs of wear on either the binding, dust jacket or pages.

Zustand: New.

Zustand: New. 2022. Paperback. . . . . .

Zustand: As New. Unread book in perfect condition.

Zustand: New. In English.

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.…

Paperback. Zustand: New. Brand new book, sourced directly from publisher. Dispatch time is 3-4 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Paperback / softback. Zustand: New. New copy - Usually dispatched within 4 working days.

Zustand: New. 2022. Paperback. . . . . . Books ship from the US and Ireland.

Zustand: New.

Paperback. Zustand: Brand New. 80 pages. 9.19x7.00x0.80 inches. In Stock.

Zustand: New. 1st edition NO-PA16APR2015-KAP.

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.…

Zustand: NEW.