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)
1
Part 1: Why Data Contracts?
4
Part 2: Driving Data Culture Change with Data Contracts
8
Part 3: Designing and Implementing a Data Architecture Based on Data Contracts

Evolving your data over time

In this section, we’ll discuss how we can manage the evolution of our data, and the schemas that define it, while still giving the data consumers the stability they need to build on the data with confidence.

We spoke in detail about how data evolves in an organization and why managing the evolution of data well is important for consumers in Chapter 4, Bringing Data Consumers and Generators Closer Together, in the Managing the evolution of data section. We also discussed the difference between a breaking change and a non-breaking change, and how for a breaking change we want to deliberately introduce some friction to ensure the migration to that new version is managed to reduce the impact on downstream consumers.

It’s this concept of versions that allows us to evolve schemas. We use versioning to track and manage the changes to a schema over time. The previous versions of the schema are used to validate whether the new version introduces...