partner-ai-dev-kit

Build PWAF-compliant ISV integrations with Databricks: OAuth, telemetry (User-Agent), Unity Catalog, JDBC, SDK, SQL drivers, REST API, Databricks Connect.

PWAF-compliant Python SQL Connector (databricks-sql-connector): PAT, OAuth M2M, OAuth U2M (custom OAuth app PKCE + token-env), credentials_provider patterns, error handling, retry logic. Use when building Python integrations that run SQL queries via a Databricks SQL warehouse.

Add a Databricks connector to an existing project that has no Databricks integration. Use when your product already exists and you want to add Databricks as a new data source or backend.

How to write a build_report.md for any PWAF connector. Captures skill traceability, sufficiency assessment, and test error/fix log. Use after implementing any connector.

How to structure a Databricks connector (REST or Python SDK): config, connect, operations, validation. Use when designing or building a new connector.

How to build and use a PWAF connector test runner (tests/run_all_tests.sh). Covers env isolation, auth types, single connector or single auth mode, parallel execution, browser tests, and report generation.

PWAF-compliant Databricks Connect (Python): PAT, OAuth M2M, OAuth U2M; serverless and classic compute. Use when building or testing Spark-over-Connect integrations.

PWAF-compliant Databricks SDK for Go (databricks-sdk-go): PAT, OAuth M2M, U2M token-env, U2M custom OAuth app (PKCE); useragent.WithProduct/WithPartner. Use when building or testing Go SDK workspace API integrations.

PWAF-compliant Databricks SQL Driver for Go (databricks-sql-go): PAT, OAuth M2M, OAuth U2M (browser + token-env + custom OAuth app PKCE), WithUserAgentEntry. Use when building or testing integrations that run SQL queries via a Databricks SQL warehouse.

PWAF-compliant Databricks JDBC Driver (databricks-jdbc v3+): PAT, OAuth M2M, OAuth U2M (custom OAuth app, token-env). Use when building or testing Java SQL warehouse integrations.

PWAF-compliant Databricks SDK for Java (databricks-sdk-java): PAT, OAuth M2M, U2M custom OAuth app, UserAgent.withProduct/withPartner. Use when building or testing Java SDK workspace API integrations.

PWAF-compliant Databricks SQL Driver for Node.js (@databricks/sql): PAT, OAuth M2M, OAuth U2M (custom OAuth app PKCE + token-env), userAgentEntry telemetry. Use when building or testing integrations that run SQL queries via a Databricks SQL warehouse.

PWAF-compliant Databricks Connect for Python: PAT, OAuth M2M, OAuth U2M (PKCE + token-env), userAgent telemetry. Use when building Spark applications that run on Databricks serverless or classic compute.

PWAF-compliant Databricks SDK for Python (databricks-sdk): PAT, OAuth M2M, U2M token-env, U2M custom OAuth app (PKCE); useragent.with_partner/with_product. Use when building or testing Python SDK workspace API integrations.

Comprehensive SQLAlchemy + Databricks patterns: PAT/M2M/U2M authentication, User-Agent telemetry, connection URL format, error handling, and retry logic. Use when building or testing Python integrations using databricks-sqlalchemy dialect.

REST API authentication for Databricks ISV integrations: PAT, OAuth M2M, U2M. Use when building or testing HTTP/REST integrations with Databricks.

PWAF User-Agent telemetry: format, per-driver configuration (Python SDK, Java SDK/JDBC, Go SDK/SQL, Node.js, REST, Databricks Connect). Use when setting up User-Agent attribution in any connector.

How to run and structure auth tests for Databricks ISV integrations: clean env per test, runner script patterns, U2M browser flows, and language-specific considerations.

U2M (user-to-machine) OAuth flows for Databricks ISV integrations: external-browser, custom-oauth-app, token-env. Covers Python, Java, Go, and Node.js implementations. Use when implementing or debugging browser-based or pre-obtained token auth.