Building High-Quality and Trusted Data Products with Databricks

by Amr Ali, Bernhard Walter, Fran Medina Castro, Glenn Wiebe, Karthik Subbarao, Lexy Kassan, Magnus Pierre and Pawarit Laosunthara

Organizations aiming to become AI and data-driven often need to provide their internal teams with high-quality and trusted data products. Building such data products ensures that organizations establish standards and a trustworthy foundation of business truth for their data and AI objectives. One approach for putting quality and usability at the forefront is through the use of the data mesh paradigm to democratize the ownership and management of data assets. Our blog posts (Part 1, Part 2) offer guidance on how customers can leverage Databricks in their enterprise to address data mesh's foundational pillars, one of which is "data as a product".

Though the idea of treating data as products may have gained popularity with the emergence of data mesh, we have observed that applying product thinking resonates even with customers who haven't chosen to embrace data mesh. Regardless of organizational structure or data architecture, data-driven decision-making remains a universal guiding principle. Data quality and usability are paramount to ensure these data-driven decisions are made on valid information. This blog will outline some of our recommendations for building enterprise-ready data products, both generally and specifically with Databricks.