Book Image

Driving Data Quality with Data Contracts

By : Andrew Jones
Book Image

Driving Data Quality with Data Contracts

By: Andrew Jones

Overview of this book

Despite the passage of time and the evolution of technology and architecture, the challenges we face in building data platforms persist. Our data often remains unreliable, lacks trust, and fails to deliver the promised value. With Driving Data Quality with Data Contracts, you’ll discover the potential of data contracts to transform how you build your data platforms, finally overcoming these enduring problems. You’ll learn how establishing contracts as the interface allows you to explicitly assign responsibility and accountability of the data to those who know it best—the data generators—and give them the autonomy to generate and manage data as required. The book will show you how data contracts ensure that consumers get quality data with clearly defined expectations, enabling them to build on that data with confidence to deliver valuable analytics, performant ML models, and trusted data-driven products. By the end of this book, you’ll have gained a comprehensive understanding of how data contracts can revolutionize your organization’s data culture and provide a competitive advantage by unlocking the real value within your data.
Table of Contents (16 chapters)
Part 1: Why Data Contracts?
Part 2: Driving Data Culture Change with Data Contracts
Part 3: Designing and Implementing a Data Architecture Based on Data Contracts

A Contract-Driven Data Architecture

In the previous chapter, we saw exactly what makes up a data contract. In this chapter, we’re going to build on that by looking at how we can use the data contract to drive our data architecture. We’ll introduce the concept of a contract-driven data architecture and show how powerful this can be. We believe this is a step-change in how we build data platforms, and we’ll discuss the many benefits we get when adopting this architecture pattern.

As part of that discussion, we’ll introduce the three principles that unlock those benefits: autonomy, guardrails, and consistency, and you’ll learn how those principles benefit the data generators, the data consumers, and the organization. To promote autonomy, we need to provide tooling that can be self-served by the data generators. We’ll finish this chapter by looking at why that is important and show an example of how to achieve it.

By the end of this chapter...