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

Are your product dashboards looking less than impressive? Are your quarterly reports falling flat? If you're facing the common problem of "good pipelines, bad data," then our book, Data Quality Fundamentals: A Practitioner's Guide to Building Trustworthy Data Pipelines, is a must-read for you. Written by Barr Moses, Lior Gavish, and Molly Vorwerck, experts from the renowned data observability company Monte Carlo, this book offers invaluable insights and proven best practices to help you overcome data quality challenges.

With our book, you'll learn how to build more trustworthy and reliable data pipelines. We'll guide you on how to write scripts that check data integrity and identify broken pipelines using data observability techniques. By implementing our strategies, you'll be able to set and maintain data SLAs, SLIs, and SLOs, ensuring the accuracy and reliability of your data.

One of the key benefits of our book is that it empowers you to develop and lead data quality initiatives within your company. You'll gain the knowledge and skills needed to treat data services and systems with the same level of care as production software, minimizing the risk of encountering faulty data in the future.

Automation is crucial when it comes to data management, and our book covers that as well. You'll discover how to automate data lineage graphs across your entire data ecosystem, providing you with comprehensive visibility and control. Additionally, you'll learn how to build anomaly detectors for your critical data assets, enabling you to proactively identify and resolve issues before they impact your business.

Don't let bad data hold you back. Take the first step towards building trustworthy data pipelines by getting your hands on Data Quality Fundamentals today!

Click here to purchase your copy and revolutionize your data infrastructure.