Most API testing tools cannot determine whether the data returned by an API is accurate, complete, or consistent with the system of record.
Get the same rigorous testing approach used for databases, files, and BI reports for your API data with iceDQ. It validates API responses, reconciles data across source and target systems, and continuously monitors API health and data quality.
API Data Validation
Validation checks the payload on its own terms - is the data itself correct, regardless of any other system.
- Response data validation: Check specific values, formats, patterns, ranges, and business rules inside the payload before it's trusted downstream. (Rule type: API Validation)
- Schema / contract validation: Confirm the response conforms to its OpenAPI or JSON Schema contract - required fields are present, data types are correct, and values fall within allowed sets. (Rule type: Validation)
- Error-response contract validation: For known failure cases, confirm the error body carries the expected shape, error codes, and messages. (Rule type: Validation)
- Response header validation: Verify Content-Type, caching directives, and correlation/trace-ID headers are present and correct. (Rule type: Validation)
- Null / default / boundary handling: Confirm optional and nullable fields, defaults, and boundary values are represented correctly in the payload. (Rule type: Validation)
- Data freshness / timestamp window: Confirm updated_at or as-of timestamps fall within the expected window, so the API is serving current, not stale, data. (Rule type: Validation)
API Data Validation
Validation checks the payload on its own terms - is the data itself correct, regardless of any other system.
- Response data validation: Check specific values, formats, patterns, ranges, and business rules inside the payload before it's trusted downstream. (Rule type: API Validation)
- Schema / contract validation: Confirm the response conforms to its OpenAPI or JSON Schema contract - required fields are present, data types are correct, and values fall within allowed sets. (Rule type: Validation)
- Error-response contract validation: For known failure cases, confirm the error body carries the expected shape, error codes, and messages. (Rule type: Validation)
- Response header validation: Verify Content-Type, caching directives, and correlation/trace-ID headers are present and correct. (Rule type: Validation)
- Null / default / boundary handling: Confirm optional and nullable fields, defaults, and boundary values are represented correctly in the payload. (Rule type: Validation)
- Data freshness / timestamp window: Confirm updated_at or as-of timestamps fall within the expected window, so the API is serving current, not stale, data. (Rule type: Validation)
API Data Reconciliation
Reconciliation checks the payload against another system - the target it loaded into, the file it should match, or another API entirely.
- API response vs. database: DiffJoin the API payload against a target table for record completeness and field-level accuracy. (Rule type: API Recon)
- API response vs. file: Convert the API payload to tabular form and compare it against a file. (Rule type: API Recon)
- API vs. backend system: Compare an OData or CDS API dataset against the underlying system of record - for example, an SAP dataset against SAP HANA via JDBC, ECC via RFC, or a Parquet extract. (Rule type: Recon)
- Multi-endpoint / nested-resource reconciliation: Reassemble multiple related REST calls per record - for example, separate endpoints for a parent record and its associated sub-resources - and reconcile the reassembled record as a whole.
- Cross-endpoint consistency: Confirm the same entity returns consistent values across every endpoint that exposes it. (Rule type: Recon)
- Filtering / query-parameter correctness: Confirm filter, sort, and query parameters return exactly the expected subset of records - no more, no less. (Rule type: Recon)
- Idempotency of POST / PUT: Confirm repeating the same write call doesn't create duplicate records or unintended side effects.
- Webhook / async callback validation: Confirm event and callback payloads match the action that triggered them, and are delivered completely and exactly once.
- API-to-API reconciliation: Compare two APIs against each other - commonly used for version migrations, re-platforming, and environment parity testing.
API Status & Health Check
Health checks validate the API's behavior as a service - the transport and protocol layer - separately from the data it returns.
- Status / health check: Execute a GET or POST call and assert a successful HTTP 200 response. (Rule type: API Validation)
- HTTP status-code assertions: Confirm each case returns the expected status - 2xx for success, 4xx for client error, 5xx for server error. (Rule type: Validation)
- Authentication / token handling: Verify OAuth2 bearer, API key, and token-refresh flows work correctly, and that missing or expired credentials correctly return 401. (Rule type: Script + Validation - a Script rule performs the auth handshake and exposes the token/status as a parameter; a Validation check asserts the expected outcome, since auth is transport behavior, not row-level data.)
- Authorization / scope (RBAC): Confirm requests outside the caller's role or scope are rejected with 403, while permitted scopes succeed. (Rule type: Script + Validation)
- Rate-limit / throttling behavior: Confirm excess requests return 429 and that Retry-After is honored on retry. (Rule type: Script + Validation)
- Latency / response-time SLA: Confirm response time stays within the agreed threshold for the endpoint. (Rule type: Script - response time is a property of the call, not the dataset.)
- Pagination completeness: Confirm all pages of a paginated response are retrieved and that the total record count matches the sum across pages, with no gaps or overlaps. (Rule type: Checksum)
PRODUCT HIGHLIGHTS
 | Connect to GET and POST APIs: |  | Configure API connections with authentication and parameters to pull response data directly into iceDQ. |
| REST, OData, and GraphQL Support: | | Purpose-built connectivity for the dominant API styles, alongside application connectors for platforms such as Salesforce and SAP. |
| Dedicated API Rule Templates: | | API Validation for field-level checks on a response, and API Recon for comparing a response against a database, file, or another API on a unique key. |
| Nested JSON Handling: | | Nested objects and arrays are parsed and flattened, so checks and reconciliations apply at any depth of the response. |
| Pagination Handling: | | Both GET and POST are supported, with multi-page responses assembled into a complete dataset before comparison. |
| Authentication Support: | | Connect to secured endpoints using the authentication scheme your APIs require, with credentials managed through secure connections rather than embedded in individual tests. |
| API-Driven Test Orchestration: | | Trigger an iceDQ rule and update a parameter value via API during rule execution, for tighter integration with external orchestration. |
| Consolidated Run Logging: | | Execute large batches of calls from an input file and log results to a single consolidated report, with the latest run auto-selected by timestamp. |
| Low-Code, No-Code, and GenAI: | | Use prebuilt checks, or auto-generate checks and SQL with iceDQ's GenAI assistant. |
| Agentic Data Testing: | | The iceDQ Agent generates API testing rules from natural language prompts and interface specifications. The iceDQ MCP Server lets AI assistants create validation and reconciliation rules, execute them, and retrieve exception reports as part of an agentic workflow. |
| Exception Reports: | | Every failed rule produces a row-level exception report identifying the exact records and fields that failed - whether they came from an API, a table, or a file. |
| Workflow Orchestration and Scheduling: | | Chain API tests into workflows triggered on a schedule or by pipeline events, and gate downstream processing on results using exit codes. |
| CI/CD and Pipeline Integration: | | Run API data tests automatically from orchestration tools and CI/CD pipelines on every deployment and integration cycle. |
Automate your API Data Testing with iceDQ.
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FAQs: API Data Testing with iceDQ
What types of APIs can iceDQ test?
iceDQ tests REST, OData, and GraphQL APIs, plus application platforms such as Salesforce accessed through their APIs.
How is iceDQ different from tools like Postman?
Functional tools test whether an API responds correctly. iceDQ tests whether the data in the response is correct, and reconciles it against databases, files, and other APIs.
Can iceDQ compare an API response with a database table?
Yes. An API Recon rule compares the response with the table row by row on a unique key, reporting missing, extra, and mismatched records.
Can iceDQ handle nested JSON responses?
Yes. Nested objects and arrays are flattened, so checks and reconciliations apply to elements at any depth.
Can iceDQ compare two APIs with each other?
Yes. API-to-API reconciliation is commonly used for version migrations, re-platforming, and environment parity testing.
Does iceDQ support authenticated APIs?
Yes. Secured endpoints are supported, with credentials stored in managed connections rather than in individual rules.
Can iceDQ test paginated API responses?
Yes. Multi-page responses are retrieved and tested as a complete dataset, so reconciliation covers the full result set.
Can iceDQ validate SaaS data like Salesforce?
Yes. Application connectors let you validate data inside SaaS platforms and reconcile it against your warehouse or lake.
What happens when an API test fails?
The rule produces a row-level exception report showing the failing records and fields, and notifications alert the responsible team.
Can API testing run automatically in our pipelines?
Yes. Rules and workflows are triggered by schedules, orchestration tools, and CI/CD pipelines, with exit codes gating downstream steps.
Does iceDQ modify the data it tests?
No. iceDQ only reads and evaluates data, then reports exceptions. It never changes data in APIs, applications, or databases.
Can business analysts create API tests without coding?
Yes. Checks can be built through the UI or generated from natural language descriptions, while engineers can add expressions for more complex logic.
Can iceDQ verify data loaded from an API into a warehouse?
Yes. Reconciling the API response against the loaded tables certifies that integration jobs moved every record accurately.
Can iceDQ test API data on a schedule?
Yes. Schedules run API tests at any frequency, from every few minutes to monthly regulatory cycles.
Is API testing separate from database and file testing in iceDQ?
No. APIs, databases, files, and BI reports are all tested in the same platform, with the same rule framework, workflows, and dashboards.
How does API data testing help with compliance?
Every rule execution is logged with results and exception reports, creating an auditable trail proving that API-sourced data was verified before use.