With data mesh adoption on the rise, our teams have been on the lookout for data platforms that treat data products as a first-class entity. DataOS is one such product. It provides end-to-end lifecycle management to design, build, deploy and evolve data products. It offers standardized declarative specs written in YAML that abstract the low-level complexity of infrastructure setup and allow developers to define the data products easily via CLI/API. It supports access control policies with ABAC and data policies for filtering and masking data. Another notable feature is its ability to federate data across a variety of data sources, which reduces data duplication and the movement of data to a central place. DataOS fits best for greenfield scenarios where it does the heavy lifting since it provides an out-of-the-box solution for data governance, data discoverability, infrastructure resource management and observability. For brownfield scenarios, the ability to orchestrate resources outside of DataOS (for example, data stacks like Databricks) is in its nascent stage and still evolving. If your ecosystem doesn’t exert a lot of opinion on data tooling, DataOS is a good way to expedite your journey for building, deploying and consuming data products in an end-to-end fashion.
