The hottest Data Contracts Substack posts right now

Data Contracts may not be widely adopted in the data engineering community.
The idea behind Data Contracts is to enforce trustworthiness and consistency in data.
The challenge with Data Contracts seems to be the complexity and adoption of specific technologies.

Data teams face challenges when separated from product engineering, leading to loss of metadata and concerns about data quality. Data contracts can help address these issues by defining the nature, completeness, and format of shared data.
Integrating data professionals within product teams can enhance understanding and usage of data, reducing the need for separate contracts. This approach allows for direct-to-consumer, organic data processes.
Centralized data platform teams can establish common standards and infrastructure, enabling embedded data personnel in product teams to work efficiently. This collaborative model streamlines data transformation and enhances data accessibility.

Understanding the Data Value Chain is essential for building successful Data Products.
Implementing Data Contracts in the Data Pipeline ensures data quality and prevents unexpected outages.
Knowing the 4 types of ML Model Deployment helps in deploying machine learning models effectively.

Data contracts are a tool to bridge the gap between software and data teams.
Big data is crucial for businesses to thrive in the data-driven world.
Introducing explainability into data-heavy products can boost user trust and engagement.

Chad Sanderson announced an upcoming book on Data Contracts with O'Reilly, covering topics like what data contracts are, how they work, implementation, examples, and the future implications. The book will delve into Data Quality and Governance.
The first two chapters of the book are available for free on the O'Reilly website. They cover the importance of data contracts and the real goals of data quality initiatives, totaling about 45 pages of content.
Chad Sanderson is currently selecting technical reviewers for the book. Interested individuals can reach out to him to share their thoughts on an advance copy.

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Producers need to move towards consumer-defined data contracts to improve data quality and alignment with user needs.
A phased approach of awareness, collaboration, and contract ownership helps in successful data contract adoption.
Starting with consumer-defined contracts drives communication, awareness, and problem visibility, leading to long-term benefits.

Healthy working agreements are essential for data quality.
Quicker feedback loops close to data sources improve data quality validation.
Celebrating the efforts of all team members contributes to maintaining clean data warehouses.