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Makini's API supports date filtering on most endpoints using query parameters. You can filter by creation date, modification date, or entity-specific date fields like order date or delivery date. Common patterns include `modified_after=2024-01-01` to retrieve records updated since a specific date, or relative timestamps like `modified_after=2024-01-01T00:00:00Z`. For optimal performance, use incremental data retrieval patterns rather than repeatedly fetching all records. The sync status endpoint provides the last sync timestamp, which you can use as the `modified_after` value for your next query. This approach minimizes data transfer and API load while ensuring you capture all changes.
Makini provides webhook testing tools in the dashboard where you can trigger test webhook deliveries to verify your endpoint configuration. Test webhooks use sample payloads matching actual event structures. Verify your endpoint receives the webhook, validates the signature correctly, and responds with a 200 status code within 10 seconds. Test webhook retries by having your endpoint return error codes or timeout, then verify Makini retries as expected. Test duplicate handling by processing the same webhook multiple times. For local development, use tools like ngrok to expose your local endpoint for webhook testing. The webhook logs in the Makini dashboard show delivery attempts, response codes, and timing, helping debug delivery issues.
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.
Our standard SLA targets 99.9% uptime for cloud deployments, which translates to less than 9 hours of downtime per year. For enterprise customers with critical integration requirements, we offer enhanced SLAs up to 99.99% through multi-region redundancy and dedicated infrastructure. SLAs cover the Makini platform itself—availability of connected third-party systems is outside our control, though we monitor their health and alert you to issues. For self-hosted deployments, uptime depends on your infrastructure configuration, and we provide architecture guidance to help you achieve your availability targets. We maintain a public status page showing real-time system health and incident history.
