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