Practice data product design vocabulary: output ports, SLA freshness guarantees, schema versioning, observability instrumentation, and catalog discoverability.
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Your data mesh design doc says 'The data product has a clearly defined output port.' What is an output port in data mesh?
In data mesh, an output port is the well-defined interface consumers use to access data. It hides the internal implementation and provides a stable contract — the producer team can change internals without breaking consumers as long as the output port contract is maintained.
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A data product spec says 'The data product SLA guarantees freshness.' What does freshness mean in this context?
Data freshness SLA defines the maximum lag — e.g., 'data is no more than 4 hours old.' Consumers need to know this to decide if the data product meets their latency requirements. A stale data product may be fine for trend reports but not for real-time dashboards.
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Your team says 'The data product schema is versioned.' Why is schema versioning important for data products?
Data products are consumed by many downstream teams. Schema versioning lets consumers pin to a version (e.g., v1) while the producer ships v2 with breaking changes — giving consumers a migration window instead of forcing immediate updates that break their pipelines.
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A data product review recommends 'adding observability instrumentation.' What should data product observability cover?
Data product observability tracks data health — not just pipeline health. Key dimensions: freshness (is the data current?), volume (are row counts as expected?), quality (are null rates and schema violations within bounds?), and lineage (where did this data come from?). These are equivalent to uptime/latency/errors for services.
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A data catalog team says 'Data product discoverability in the catalog is a first-class requirement.' Why?
In data mesh, the data catalog is the discovery layer. Consumers must be able to find data products, understand their schemas, check SLAs, see ownership, and access documentation — all without asking the producing team. Without good discoverability, the self-serve model breaks down.