Defines operations, typed inputs and outputs, authorization context, errors, compatibility, and limits for data warehouse analytics connector, so adopters can provide one portable metric-plan adapter contract for governed cloud or on-premise analytical ware...
Package status: reference context ready for human review. The contract and test scenarios are complete, but no claim is made that an adopting implementation has passed them.
Decision
For Data warehouse analytics connector, define a closed, versioned interface contract so adopters can provide one portable metric-plan adapter contract for governed cloud or on-premise analytical warehouses. The abstraction normalizes safety and result semantics; it does not pretend that vendors have identical SQL, cost, latency, freshness, or cancellation behavior. This is a reference contract: database products, numeric budgets, jurisdictions, retention, organizational defaults, and accountable owners remain explicit adoption choices.
Scope
- The Data warehouse analytics connector actors, inputs, outputs, states, versions, and externally visible outcomes needed to provide one portable metric-plan adapter contract for governed cloud or on-premise analytical warehouses.
- The role-specific focus of this block: define a closed, versioned interface contract, including primary, cached, asynchronous, export, support, and recovery paths where applicable.
- Adoption-specific configuration, ownership, rollout, evidence retention, and review responsibilities needed to use the contract safely.
Outside this block
- The abstraction normalizes safety and result semantics; it does not pretend that vendors have identical SQL, cost, latency, freshness, or cancellation behavior.
- Choosing a universal database, model, renderer, vendor, numeric threshold, retention period, timezone, jurisdiction, or service-level objective.
- Claiming that packaged scenarios ran against a downstream implementation or that this reference grants security, privacy, accessibility, analytical, or legal approval.
Contract
- The declared Data warehouse analytics connector contract requires this rule: Each adapter publishes capabilities for grains, dimensions, functions, precision, dry run, estimated cost, asynchronous jobs, cancellation, paging, snapshots, and freshness.
- The declared Data warehouse analytics connector contract requires this rule: The gateway compiles only supported metric-plan operations against curated versioned views and never forwards model-authored SQL or arbitrary warehouse identifiers.
- The declared Data warehouse analytics connector contract requires this rule: Warehouse credentials are scoped to minimum read access, isolated per environment or policy boundary, rotated outside prompts, and unavailable to the model.
- The declared Data warehouse analytics connector contract requires this rule: Where supported, dry-run or cost estimation occurs before execution; otherwise a conservative declared budget and pre-aggregated path are required.
- The declared Data warehouse analytics connector contract requires this rule: Asynchronous queries use opaque job handles with caller binding, status, expiry, cancellation, terminal errors, and authorization rechecks on retrieval.
- The declared Data warehouse analytics connector contract requires this rule: The portable result contract states exact or approximate precision, sampling, snapshot, watermark, timezone, missing buckets, truncation, and partial status.
- The declared Data warehouse analytics connector contract requires this rule: Adapter-specific errors map to stable semantic categories while safe diagnostic references preserve enough detail for operators.
- The declared Data warehouse analytics connector contract requires this rule: Conformance fixtures prevent a new adapter from claiming compatibility until metric values, policy behavior, budgets, and failure semantics match the gateway contract.
Implementation guidance
- Design operations around stable domain intent instead of exposing database or vendor primitives to callers.
- Use closed schemas, canonical identifiers, explicit version negotiation, and stable error codes at the public boundary.
- Document which identity and authorization facts come from trusted context and which fields are only untrusted request parameters.
- Provide compatibility fixtures for every supported client, connector, and result-schema version.
Failure handling and safeguards
- For Data warehouse analytics connector, An unsupported function, dimension, consistency level, cost estimate, or cancellation promise is rejected rather than emulated with different semantics.
- For Data warehouse analytics connector, An over-budget estimate, expired job, revoked identity, partial partition, or stale snapshot reaches a visible bounded result state.
- For Data warehouse analytics connector, If adapter capability metadata is missing or stale, routing stops before execution and names the operator-owned compatibility gap.
Verification and operations
- For the api evidence of Data warehouse analytics connector, run the same frozen metric plans through every supported adapter and compare values, periods, nulls, precision, quality flags, and error classes.
- For the api evidence of Data warehouse analytics connector, test credential scope, row and column policies, job-handle isolation, cancellation, paging, export, and cache reuse under multiple roles.
- For the api evidence of Data warehouse analytics connector, record predicted and actual scan, cost, latency, result size, and freshness so routing budgets can be calibrated from evidence.
Adoption assumptions
- The adopting product has authenticated identity, a versioned authorization policy, owned metric definitions, bounded telemetry, and a controlled path for change.
- Names and values in the example are fictional adoption fixtures, not universal defaults, production credentials, performance promises, or business targets.
- Referenced specifications constrain protocol, security, accessibility, or vendor behavior; the adopting team must confirm current applicability before promotion.
The executable-looking examples in this package are fixtures and acceptance contracts. Run the collection validator to check structure and metadata, then translate and execute the scenarios in the target repository before recording implementation evidence.
References
- Google Cloud BigQuery documentation, Run a query (applies as of 2026-09-12)
- OWASP Authorization Cheat Sheet