Want a complete guide to the ins and outs of monitoring data quality? Today, we’re sharing an exclusive first look at our book, Automating Data Quality Monitoring at Scale, which will be published by O’Reilly Media later this year.
The full book will be available to purchase, but we’re releasing an early preview now at no cost.
About the book
Automating Data Quality Monitoring at Scale is based on everything we’ve learned from building Anomalo, and shares a lot of the “secret sauce” behind our approach. We’re thrilled that O’Reilly, with expertise in technical education, is helping us translate our knowledge into a resource for data-driven organizations everywhere.
In Chapter 1, “The Data Quality Imperative,” you’ll learn:
- Cautionary tales of the impact of uncaught data issues
- What are the main factors that erode data quality
- The definition of “data shocks” and “data scars” and how they impact BI and AI/ML
…and much more.
In future chapters, we’ll also share:
- How to apply unsupervised machine learning models for detecting data issues
- How to implement notifications while avoiding alert fatigue
- How to integrate data quality monitoring with data catalogs, orchestration layers, and other systems
- How to deploy, manage, and maintain your monitoring solution
Note that the contents of this preview will almost certainly change as we continue to craft the book and get feedback from early readers. If you have ideas to share or notice missing content, please feel free to drop us a note email@example.com.