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Makini uses standard HTTP status codes and structured error responses. Error responses include an error code (e.g., `AUTHENTICATION_FAILED`, `RATE_LIMIT_EXCEEDED`), error type for categorization, a human-readable error message, and a unique request ID for support inquiries. Common status codes include 400 for invalid requests, 401 for authentication failures, 403 for permission issues, 429 for rate limiting, 500 for server errors, and 503 for service unavailability. Use the error code for programmatic error handling rather than parsing error messages. The request ID helps our support team quickly identify and investigate specific issues.
Makini supports create, read, update, and delete (CRUD) operations, though availability varies by system and entity type. Most systems support creating and updating core entities like purchase orders, work orders, and inventory items. Read operations are universally supported across all entity types. Delete operations are less commonly supported due to system constraints—many industrial systems use soft deletes or status changes rather than true deletion. Update operations may be limited to specific fields depending on system configuration and business rules. For example, some systems prevent modifying purchase orders after approval. We recommend validating specific operation support for your use case during the technical deep dive.
Makini uses cursor-based pagination for retrieving large datasets. API responses include a `next_cursor` field when additional results are available. To retrieve the next page, include the cursor value in your next request: `GET /api/v1/purchase-orders?cursor=CURSOR_VALUE`. Cursor-based pagination is more reliable than offset-based pagination because it handles data changes between requests—if records are added or deleted while you're paginating, you won't miss records or see duplicates. Page size is configurable up to a maximum limit (typically 100-500 records per page depending on entity type). For optimal performance, use the largest page size your application can handle efficiently. The API response also includes total count when available from the source system.
Yes, through Makini Flows, which includes connectors for popular databases including Snowflake, PostgreSQL, MySQL, MongoDB, and others. This enables workflows that synchronize data between industrial systems and your data warehouse or analytics platforms. For example, you can sync purchase orders from SAP to Snowflake for analytics, or use database queries to drive integration logic. Database integrations use the same workflow builder as other integrations, making it easy to combine industrial system data with database operations. For direct database-to-database syncing, we can help design optimized workflows. Database connections are treated as custom integrations and may require additional workflow development.
