Practical Data Quality

This book explains how to implement a data lake strategy, covering the technical and business challenges architects commonly face. It also illustrates how and why client requirements should drive architectural decisions.

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.

Between major privacy regulations like the GDPR and CCPA and expensive and notorious data breaches, there has never been so much pressure to ensure data privacy. Unfortunately, integrating privacy into data systems is still complicated. This essential guide will give you a fundamental understanding of modern privacy building blocks, like differential privacy, federated learning, and encrypted computation. Based on hard-won lessons, this book provides solid advice and best practices for integrating breakthrough privacy-enhancing technologies into production systems.

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