Databricks shares best practices for building high-quality data products

Databricks has published a comprehensive guide outlining recommendations for building high-quality and trusted data products, both generally and specifically with its platform. The post advocates for applying product thinking to data assets to establish a trustworthy foundation for AI and data-driven objectives across organizations. It details five key characteristics for data products—quality, semantic consistency, privacy, security, and discoverability—and describes a typical data product lifecycle. This guidance is crucial for data product owners, engineers, and architects aiming to enable robust, data-driven decision-making and democratize data ownership.

• →Security (who is allowed to use the data product)