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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
A propos de l'auteur
Barr Moses is the CEO and co-founder of Monte Carlo, a data reliability company. In her decade-long career in data, Barr has served as commander of a data intelligence unit in the Israeli Air Force, a consultant at Bain & Company, and VP of Operations at Gainsight, where she built and led their data and analytics team. The instructor of O'Reilly first course on Data Observability, an emerging discipline in data engineering, 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.
