




Philips Hue manages smart lighting systems, allowing users to automate lighting scenes, schedules, and integrations for connected home environments.
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API tokens must be stored securely and should never be exposed on the client side or in public repositories. Store tokens in secure environment variables or dedicated secrets management systems like AWS Secrets Manager, HashiCorp Vault, or Azure Key Vault. Never hardcode tokens in application code or commit them to version control. Implement proper access controls so only authorized services can access stored tokens. For production environments, use separate tokens from development/testing environments. Rotate tokens periodically and immediately revoke tokens if you suspect they've been compromised. Makini tokens provide access to customer data, so treat them with the same security standards you'd apply to database credentials.
Authentication errors (401 status code) typically occur for a few reasons: the API token is invalid or expired, the underlying system credentials have changed, the system requires reauthorization, or the token lacks necessary permissions. First, verify you're including the token correctly in the Authorization header. Check the connection status in the Makini dashboard—if it shows as requiring reauthorization, the customer needs to reconnect. If credentials were recently changed in the source system, you'll need to reconnect to obtain a new token. For persistent issues, check if the system account has sufficient permissions to perform the requested operation. If the problem continues, contact support with the request ID for investigation.
Testing should cover authentication, data retrieval, data writing, error handling, and workflow logic. Start by connecting a test system through Makini's authentication flow. Use sandbox or non-production instances of your target systems when available. Test API calls for each entity type you'll use (purchase orders, work orders, etc.) to verify data mapping and field coverage. Test error scenarios by providing invalid inputs or attempting operations without proper permissions. For workflow-based integrations, test each workflow step independently before testing end-to-end. Verify webhook delivery and signature verification. Test with realistic data volumes to identify performance issues. Include tests for connection failure scenarios and verify your error handling and retry logic work correctly.
For bulk operations, we recommend batch processing with appropriate rate limiting and error handling. Makini Flows provides built-in batch processing capabilities with configurable batch sizes, delays between batches, and error handling. For API-based bulk operations, implement pagination when retrieving large datasets—our API returns results in pages with continuation tokens for fetching subsequent pages. When writing large volumes of data, break operations into smaller batches (typically 50-100 records per batch) with delays between batches to avoid overwhelming the target system. Implement comprehensive error logging to identify which specific records fail in a batch. For very large operations (thousands of records), consider asynchronous processing patterns where you queue operations and process them in the background.
