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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

Introducing the principles of a contract-driven data architecture

Building a contract-driven data architecture provides many benefits to both the data generators and consumers, and the wider organization. These benefits are achieved through these three principles:

  • Automation
  • Guidelines and guardrails
  • Consistency

Let’s look at each of these in turn.

Automation

There are several common tasks that need to be carried out on the data and the resources we use to manage it, no matter what that data is and who owns it. These tasks are great candidates to automate, reducing the effort the data generators need to spend managing the data.

The resources required for our data will almost always include the tables in the data warehouse. We can use the data contract to automate the creation and management of that table, for example, by creating the table when the contract is created and keeping the schema of the table in sync with the schema in the contract...

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