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O’Reilly Announces First-Ever Book on Data Quality by Monte Carlo’s Barr Moses and Lior Gavish to Help Data Teams Achieve Reliability at Scale

Available today, the pre-release chapters dive into how some of the best teams are architecting for data observability.

Monte Carlo, the data reliability company, announced the launch of Data Quality Fundamentals: A Practitioner’s Guide to Building More Trustworthy Data Pipelines, published by O’Reilly Media and available for free on the Monte Carlo website. This is the first book released 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.

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“As data pipelines grow increasingly complex, the requirements around data quality and trust are higher than ever, and yet, many data teams don’t have the necessary resources to execute on this vision”

For decades, teams have struggled to measure, maintain, improve, and predict data quality, and over the past few years, the speed and scale at which we ingest, process, transform, and analyze data have made these challenges even harder. This lack of visibility into the health of our data leads to data downtime, periods of time when data is missing, inaccurate, or otherwise erroneous, and a leading reason why data quality initiatives fail.

O’Reilly’s Data Quality Fundamentals is the only guide of its kind to help data engineers and analysts understand the key factors that contribute to unreliable data pipelines and poor data quality, leverage new and novel processes and technologies to solve these problems, and design resilient, observable systems to prevent data downtime from happening in the first place.

In this book, readers will learn:

  • Why data quality is critical for deriving true value from your analytics
  • How to achieve high data quality across the organization with data observability
  • How to architect a scalable, end-to-end data observability platform
  • The people, processes, and frameworks necessary for a robust data quality strategy
  • How to ensure data quality at scale with DevOps principles
  • Best practices for building data trust gleaned from real-world examples

The book is co-authored by Barr Moses, CEO and co-founder of Monte Carlo, Lior Gavish, CTO and co-founder of Monte Carlo, and Molly Vorwerck, Head of Content at Monte Carlo and former lead editor of the Uber Engineering Blog.

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“As data pipelines grow increasingly complex, the requirements around data quality and trust are higher than ever, and yet, many data teams don’t have the necessary resources to execute on this vision,” said Moses. “Lior, Molly, and I were inspired to write this book after our experiences building reliable data systems and working with some of the best data leaders to help them accelerate the adoption of data at their organizations and eliminate data downtime through data observability. We’re honored to partner with O’Reilly on this landmark contribution to the data engineering canon.”

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