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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

Getting started with data contracts

In this section, we’re going to look at exactly how to get started with implementing data contracts in your organization. We’ll learn how to identify a use case, prove the concept, and build the minimum required tooling.

The first step is to decide on your key objective(s) for implementing data contracts in your organization. What are the problems you want to solve first, and why are they important to the business?

This could be improving the dependability and performance of your data pipelines, maybe because you are seeing that users are losing trust in the data they are being provided and lack confidence in using it to support their decision-making. Or, it could be you want to make your data more accessible and easier to use in business-critical applications, including machine learning applications, as the development and successful deployment of those applications form an important part of the business goals.

Whatever it...

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