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A01=Barr Moses
A01=Lior Gavish
A01=Molly Vorwerck
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Author_Barr Moses
Author_Lior Gavish
Author_Molly Vorwerck
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Category1=Non-Fiction
Category=UNC
Category=UNF
COP=United States
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Language_English
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Price_€50 to €100
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Data Quality Fundamentals: A Practitioner''s Guide to Building Trustworthy Data Pipelines

★★★★★
★★★★★

English

By (author): Barr Moses Lior Gavish Molly Vorwerck

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 See more
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A01=Barr MosesA01=Lior GavishA01=Molly VorwerckAge Group_UncategorizedAuthor_Barr MosesAuthor_Lior GavishAuthor_Molly Vorwerckautomatic-updateCategory1=Non-FictionCategory=UNCCategory=UNFCOP=United StatesDelivery_Delivery within 10-20 working daysLanguage_EnglishPA=AvailablePrice_€50 to €100PS=Activesoftlaunch
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Product Details
  • Dimensions: 178 x 233mm
  • Publication Date: 30 Sep 2022
  • Publisher: O'Reilly Media
  • Publication City/Country: United States
  • Language: English
  • ISBN13: 9781098112042

About Barr MosesLior GavishMolly Vorwerck

Barr 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 Bain & Company and VP of Operations at Gainsight where she built and led their data and analytics team. The instructor of O'Reilly first course on Data Observability an emerging discipline in data engineering Barr has worked with hundreds of data teams struggling with these problems. Inspired by her time in the analytics trenches she is building a product literally dedicated to identifying resolving and preventing what she calls data downtime periods of time when data is missing erroneous or otherwise inaccurate. In other words: bad data. In this book she shares her experiences and learnings on how today's data organizations can achieve high data quality at scale through technological organization and cultural best practices. Lior Gavish is CTO and Co-Founder of Monte Carlo a data reliability company backed by Accel Redpoint GGV and other top Silicon Valley investors. Prior to Monte Carlo Lior co-founded cybersecurity startup Sookasa which was acquired by Barracuda in 2016. At Barracuda Lior was SVP of Engineering launching award-winning ML products for fraud prevention. Lior holds an MBA from Stanford and an MSC in Computer Science from Tel-Aviv University. Molly Vorwerck is the Head of Content at Monte Carlo a data reliability company. Prior to joining Monte Carlo Molly served as editor-in-chief of the Uber Engineering Blog and lead program manager for Uber's Technical Brand team where she spent countless hours helping engineers data scientists and analysts write and edit content about their technical work and experiences. She also led internal communications for Uber's Chief Technology Officer and strategy for Uber AI's Research Review Program. In her spare time she freelances for USA Today reads up on all the latest trends in data and volunteers for the California Historical Society.

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