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Data Quality Fundamentals

Free O’Reilly Book

Data Quality Fundamentals

New Chapter Release: Fixing Data Quality Issues at Scale

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. 

We are excited to announce the early release of Data Quality Fundamentals: A Practitioner’s Guide to Building More Trustworthy Data Pipelines

In Chapter 1 and Chapter 2, we discussed why data quality deserves attention today, and walked through how data engineers and analysts can architect more reliable data ecosystems, from ingestion in the warehouse or lake to the analytics layer downstream. 

In Chapter 3, we showed what it takes to identify, alert for, resolve, and prevent data quality issues in a holistic and end-to-end way across your stack. 

You can now access Chapters 4 and 6, “Preventing Broken Data Systems” and “Democratizing Data Quality” for free (a $67 value). You will learn how to:

  • Design and architect more reliable data systems
  • Build your own data catalog from scratch
  • Set up Great Expectations, dbt, and Airflow data quality checks 
  • Assign ownership for data assets
  • Create accountability for data reliability with SLAs, SLIs, and SLOs
  • And more!

We’re thrilled to share these new chapters with you for FREE. Enjoy!

Download Chapters 1-4, 6 for Free

Meet The Authors

Barr Moses

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.

Molly Vorwerck

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.

Lior Gavish

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.