Microsoft, Google back Apache Ossie to make enterprise data and AI platforms more interoperable
Short summary
Microsoft and Google are supporting Apache Ossie, an open specification that lets enterprises move semantic models between analytics platforms without rebuilding them. The backing from major vendors makes portability more likely, but governance gaps and the spec's early stage mean enterprises still face real constraints in switching platforms.
- Microsoft is building two-way converters between Power BI and Ossie, and pushing for DAX language support, so calculation logic moves with models across platforms instead of needing recreation.
- Governance features like row-level security and access policies aren't included in the current spec, forcing enterprises to reconfigure these on each platform even with portable models.
- Ossie uses a hub-and-spoke model where the spec acts as a common format and individual vendors provide converters, reducing engineering overhead compared to point-to-point translations.
- The specification is still in development (version 0.2), lacks capabilities large enterprises need like cross-model references, and won't eliminate vendor lock-in—just shift it to platform-specific execution behavior.
Full summary
Microsoft and Google are backing Apache Ossie, an open specification project that lets companies move semantic models (the definitions of datasets, metrics, and business logic) between different analytics platforms without rebuilding everything from scratch. Over 60 companies including Databricks, Snowflake, Tableau, and Salesforce are already involved. The project uses JSON and YAML to create a common format, then relies on converters that translate between this standard and each vendor's proprietary system—a hub-and-spoke approach that beats building separate point-to-point converters for every platform combination. Microsoft is building a two-way converter for Power BI and pushing to add support for DAX, its calculation language. Google is joining the effort and already has BigQuery/GoogleSQL listed as a supported dialect.
The practical payoff matters: enterprises could stop rebuilding the same business metrics and calculations every time they switch platforms or add new tools. That reduces what analysts call "metric drift"—the problem of maintaining identical definitions across multiple systems and getting different results. For companies deploying AI agents, a single definition of a metric across platforms means less risk of agents interpreting the same number differently. But the benefits have hard limits. Portability doesn't equal true interoperability. A metric might convert syntactically but still produce different results if the destination platform handles joins, nulls, or time calculations differently. Power BI's sophisticated time-intelligence functions and calculation groups don't map cleanly to standard SQL, so complex models could lose behavior in translation. Governance gaps are another problem—the current spec covers datasets and metrics but leaves row-level security and access policies to be set up again on each platform.
Ossie is still in Apache Incubator status, meaning it's not yet a full project and the specification (currently version 0.2) is explicitly a development draft that could change. That uncertainty matters for CIOs considering it as a long-term strategy. Even if adoption spreads, vendor lock-in probably won't disappear entirely—it'll just shift. Platforms will keep proprietary execution engines and vendor-specific features that no interchange format can capture. Enterprises get portable definitions but platform-specific behavior, which means they still can't freely move between systems without testing and validation, especially for anything complex.