Data Quality Fundamentals | Agenda Bookshop Skip to content
A01=Barr Moses
A01=Lior Gavish
A01=Molly Vorwerck
Age Group_Uncategorized
Age Group_Uncategorized
Author_Barr Moses
Author_Lior Gavish
Author_Molly Vorwerck
automatic-update
Category1=Non-Fiction
Category=UNC
Category=UNF
COP=United States
Data Data Quality data pipelines Data Observability Data Engineering Data Analytics Data Science
Delivery_Delivery within 10-20 working days
eq_computing
eq_isMigrated=2
eq_non-fiction
Language_English
PA=Available
Price_€50 to €100
PS=Active
softlaunch
Product Image

Data Quality Fundamentals

★★★★★
★★★★★

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
€65.99
A01=Barr MosesA01=Lior GavishA01=Molly VorwerckAge Group_UncategorizedAuthor_Barr MosesAuthor_Lior GavishAuthor_Molly Vorwerckautomatic-updateCategory1=Non-FictionCategory=UNCCategory=UNFCOP=United StatesData Data Quality data pipelines Data Observability Data Engineering Data Analytics Data ScienceDelivery_Delivery within 10-20 working dayseq_computingeq_isMigrated=2eq_non-fictionLanguage_EnglishPA=AvailablePrice_€50 to €100PS=Activesoftlaunch
Delivery/Collection within 10-20 working days
Product Details
  • Dimensions: 178 x 233mm
  • Publication Date: 30 Sep 2022
  • Publisher: O'Reilly Media
  • Publication City/Country: US
  • 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.

Customer Reviews

Be the first to write a review
0%
(0)
0%
(0)
0%
(0)
0%
(0)
0%
(0)
We use cookies to ensure that we give you the best experience on our website. If you continue we'll assume that you are understand this. Learn more
Accept