Data Quality Fundamentals
Free O’Reilly Book
Data Quality Fundamentals
Claim your early release copy (a $67 value)
Barr, Molly and Lior have been hard at work with O’Reilly Media drafting what we hope will be the definitive guide on how data teams architect systems to achieve reliable data at scale.
A Practitioner’s Guide to Building Trustworthy Data Pipelines
O’Reilly’s Data Quality Fundamentals is the definitive guide and first book published by O’Reilly to educate the market on how best-in-class data teams design and architect technical systems to achieve trustworthy and reliable data at scale.
For decades, the lack of visibility into the health of our data has led to data downtime, periods of time when data is missing, inaccurate, or otherwise erroneous, and a leading reason why data quality initiatives fail.
This is the only guide of its kind to help data engineers and analysts understand the key factors that contribute to poor data quality and how to detect, resolve, and prevent these issues at scale.
We are excited to announce the official release of the completed book is now available for free download.
Download All Chapters for Free
Access your copy to learn:
- Why data quality deserves attention and what exactly is the concept of data downtime.
- How data engineers and analysts can architect more reliable data ecosystems, from ingestion in the warehouse or lake to the analytics layer downstream.
- What it takes to identify, alert for, resolve, and prevent data quality issues in a holistic and end-to-end way across your stack.
- Technical solutions for preventing broken data systems, including how to pull data quality metrics and tools for collecting, cleaning and transforming data.
- Real-world case studies and approaches from leading companies like Intuit, Uber, and Fox.
- And much more!
We’re thrilled to share these new chapters with you for FREE. Enjoy!
Meet The Authors
CEO and Co-founder of Monte Carlo
Barr Moses is the CEO and co-founder of Monte Carlo, a data reliability company. 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.
Head of Content at Monte Carlo
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.
CTO and Co-founder of Monte Carlo
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.