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

Summary

In this chapter, we walked through a sample implementation of a data-contract-driven architecture and used that to illustrate the concepts we have been learning throughout the book and show them in action. We started by defining a contract in a custom YAML-based interface, and used that to drive a few different applications and services.

The first of those was a BigQuery table, which acts as the interface between the data generators and the consumers. We introduced an IaC tool called Pulumi and showed how it can be used to create and manage resources driven by the data contract.

We then showed how, by converting our data contract to JSON Schema, an open standard, we can easily produce libraries to help the data generators publish data that matches the schema and passes the data quality checks we defined.

That same JSON Schema was then used to populate a schema registry. We showed how that allows the schemas to be easily accessible through its rich API and also looked...