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Driving Data Quality with Data Contracts

Driving Data Quality with Data Contracts

By : Andrew Jones
4.8 (11)
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Driving Data Quality with Data Contracts

Driving Data Quality with Data Contracts

4.8 (11)
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)
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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

Summary

In this chapter, we introduced the concept of a contract-driven data architecture. This is an architecture driven by data contracts and the metadata we define within them. We showed how powerful this idea is, and why we believe it’s a step-change in how we build data platforms.

We use this pattern to build more generic data tooling, where instead of building similar pipelines as point solutions we can build tooling that doesn’t mandate anything about the data and how it is structured if we have enough context about the data, defined as metadata in the data contract. When adopting this pattern, it’s recommended to build a data infrastructure team, whose remit is to build this tooling for the adoption of all data generators, wherever they are in the organization.

To illustrate how this pattern is different from how we built platforms before, we walked through a case study of a previous service we implemented at GoCardless, the Data Platform Gateway...

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