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

Technical requirements

You can find all the code for this chapter in the Chapter08 directory in the GitHub repository at https://github.com/PacktPublishing/Driving-Data-Quality-with-Data-Contracts.

To follow along with the code samples in this chapter, download the code from GitHub. Ensure you have Python version 3.9.12 or above installed on your local machine, and then run the following commands from the Chapter08 directory to install the dependencies in a virtual environment:

$ python3 -m venv venv
$ source venv/bin/activate
$ pip install -r requirements.txt

You’ll also need a Google Cloud account. If you don’t have one already, you can sign up at https://cloud.google.com. Once created, you will need to ensure you have permission to create BigQuery datasets and tables in your project by following the instructions at https://cloud.google.com/bigquery/docs/access-control.

Next, install the gcloud CLI by following the instructions at https://cloud.google.com...