O’Reilly Publishes Data Quality Fundamentals by Monte Carlo Founders to Help Teams Architect More Reliable Data Systems
SAN FRANCISCO--(BUSINESS WIRE)--Oct 4, 2022--
Monte Carlo, the data reliability company, today announced the launch of Data Quality Fundamentals: A Practitioner's Guide to Building More Trustworthy Data Pipelines, a book published by O’Reilly Media and available for free on the Monte Carlo website.
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O'Reilly Data Quality Fundamentals: A Practitioner's Guide to Building Trustworthy Data Pipelines (Graphic: Business Wire)
This is the first O’Reilly book released which explains how best-in-class data teams design and architect technical systems to achieve trustworthy and reliable data at scale.
For decades, teams have struggled to measure, maintain, improve and predict data quality. Over the past few years, the speed and scale at which organizations ingest, process, transform and analyze data have made these challenges even more difficult. In fact, a recent Wakefield Research survey found that data professionals spend a whopping 40% of their time managing data quality, and that poor data quality impacts upwards of 26% of their companies’ revenue.
This lack of visibility into the end-to-end data health of 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 helps data engineers and analysts understand the critical factors underpinning poor data quality. It also includes invaluable advice for applying cutting-edge technologies to existing data stacks, and building resilient, observable systems to prevent data downtime from occurring in the first place.
Readers will learn:
- Why data quality deserves attention now
- How data engineers and analysts can architect more reliable data ecosystems
- What it takes to identify, alert for, resolve and even prevent data downtime
- Technical solutions for conducting root cause and impact analysis on data pipelines
- The critical differences between data quality monitoring and data observability
- Real-world case studies in achieving high quality data from companies like Intuit, Uber, and Fox
- How data lakehouses, data mesh architectures, automation and other trends will impact the future of reliable data
The book was 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.
“In the coming years, reliable data will become even more critical for organizations, regardless of your stack or industry. We hope that this book will prepare the next generation of data teams to navigate these challenges as they drive data product development and analytics strategy forward for their business,” said Moses. “It has been an honor to work and learn from other experts, including practitioners and data leaders from some of the most innovative companies, about the processes, culture and teams they’re building to achieve data trust at scale. I’m excited to see what’s next for other organizations, especially after their data engineers and analysts read our book.”
Get Free Access Today
All chapters of Data Quality Fundamentals (a $67 value) - including a bonus conclusion about the top five trends shaping the future of reliable data - are available for free from Monte Carlo.
Visit https://www.montecarlodata.com/oreilly-data-quality-fundamentals-early-release/ today to get early access to the book.
About the Authors
Barr Moses is CEO & co-founder of Monte Carlo, a data reliability company and creator of the industry leading Data Observability platform, backed by Accel, GGV, Redpoint, ICONIQ Growth, Salesforce Ventures, and other top Silicon Valley investors. Previously, she was VP Customer Operations at customer success company Gainsight, where she helped scale the company 10x in revenue and built the data/analytics team from scratch. She also served in the Israeli Air Force as a commander of an intelligence data analyst unit. Barr graduated from Stanford University with a B.Sc. in Mathematical and Computational Science.
Lior Gavish is CTO and co-founder of Monte Carlo. 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 M.Sc. in Computer Science from Tel-Aviv University.
Molly Vorwerck is Head of Content and Communications at Monte Carlo. Prior to joining Monte Carlo, Molly led the Tech Brand and Content team at Uber, where she served as lead editor for the Uber Engineering Blog and Uber Research Program. She graduated from Stanford University with a B.A. in American Studies and wrote her honors thesis on Elvis Presley.
About Monte Carlo
As businesses increasingly rely on data to power digital products and drive better decision-making, it’s mission-critical that this data is accurate and reliable. Monte Carlo, the data reliability company, is the creator of the industry's first end-to-end Data Observability platform. Named an Enterprise Tech 30 company in 2021 and 2022, a 2021 IDC Innovator, an Inc. Best Place Workplace for 2021 and 2022, and a "New Relic for data" by Forbes, we've raised $325M from Accel, ICONIQ Growth, GGV Capital, Redpoint Ventures, IVP, and Salesforce Ventures. Monte Carlo works with data-driven companies like Fox, The New York Times, Vizio, CreditKarma, and other leading enterprises to help them achieve trust in data.
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