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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

Data contract publishing patterns

Data generators need to be able to publish their data easily and reliably to the interface they are providing to their data consumers, which will typically be a table in a data warehouse or lakehouse, such as Snowflake or Google BigQuery, or a topic in an event streaming platform such as Apache Kafka or Google Pub/Sub.

In this section, we’ll look at the different patterns they can use to publish their data to these systems, and the pros and cons of each.

Perhaps the key consideration you need to make is whether you need a transactional guarantee between the source system and the interface you’re providing to the data consumer. It’s what ensures consistency between the data in our service and the data used by our data consumers.

Consider the scenario where you have a user of the system taking some action that results in a new record being written to the services database – for example, placing an order. Writing to...

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