Automating Data Quality Monitoring at Scale: Going Deeper Than Data Observability

Stanley, Jeremy/ Schwartz, Paige

ISBN 10: 1098145933 / ISBN 13: 9781098145934

Published by Oreilly & Associates Inc, 2024

About this Item

170 pages. 9.19x7.00x0.46 inches. In Stock.

Seller Inventory # x-1098145933

Bibliographic Details

• Title: Automating Data Quality Monitoring at Scale: Going Deeper Than Data Observability
• Publisher: Oreilly & Associates Inc
• Publication Date: 2024
• Language: English
• ISBN 10: 1098145933
• ISBN 13: 9781098145934
• Binding: Paperback
• Condition: Brand New
• Weight: 0.38 kilograms

About this title

Synopsis

The world's businesses ingest a combined 2.5 quintillion bytes of data every day. But how much of this vast amount of data--used to build products, power AI systems, and drive business decisions--is poor quality or just plain bad? This practical book shows you how to ensure that the data your organization relies on contains only high-quality records.

Most data engineers, data analysts, and data scientists genuinely care about data quality, but they often don't have the time, resources, or understanding to create a data quality monitoring solution that succeeds at scale. In this book, Jeremy Stanley and Paige Schwartz from Anomalo explain how you can use automated data quality monitoring to cover all your tables efficiently, proactively alert on every category of issue, and resolve problems immediately.

This book will help you:

• Learn why data quality is a business imperative
• Understand and assess unsupervised learning models for detecting data issues
• Implement notifications that reduce alert fatigue and let you triage and resolve issues quickly
• Integrate automated data quality monitoring with data catalogs, orchestration layers, and BI and ML systems
• Understand the limits of automated data quality monitoring and how to overcome them
• Learn how to deploy and manage your monitoring solution at scale
• Maintain automated data quality monitoring for the long term

About the Authors

Jeremy Stanley is co-founder and CTO at Anomalo. Prior to Anomalo, Jeremy was the VP of Data Science at Instacart, where he led machine learning and drove multiple initiatives to improve the company's profitability. Previously, he led data science and engineering at other hyper-growth companies like Sailthru. He's applied machine learning and AI technologies to everything from insurance and accounting to ad-tech and last-mile delivery logistics. He's also a recognized thought leader in the data science community with hugely popular blog posts like Deep Learning with Emojis (not Math). Jeremy holds a BS in Mathematics from Wichita State University and an MBA from Columbia University.

Paige Schwartz is a professional technical writer at Anomalo who has written for clients such as Airbnb, Grammarly, and OpenAI. She specializes in communicating complex software engineering topics to a general audience and has spent her career working with machine learning and data systems, including 5 years as a product manager on Google Search. She holds a joint BA in Computer Science and English from UC Berkeley.