Data Quality Fundamentals: A Practitioner's Guide to Building Trustworthy Data Pipelines

Barr Moses · Lior Gavish · Molly Vorwerck

09/2022 · "O'Reilly Media, Inc."

Acerca deste livro eletrónico

Do your product dashboards look funky? Are your quarterly reports stale? Is the data set 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 these questions, 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 observability 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
• Learn how to set and maintain data SLAs, SLIs, and SLOs
• Develop and lead data quality initiatives at your company
• Learn how to treat data services and systems with the diligence of production software
• Automate data lineage graphs across your data ecosystem
• Build anomaly detectors for your critical data assets

Acerca do autor

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.