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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.
Data synchronization frequency is configurable based on your requirements. For real-time needs, Makini supports webhook-based synchronization where changes trigger immediate updates. For scheduled syncing, common intervals range from every 15 minutes to daily, depending on data volume and business requirements. The initial sync after connecting a system retrieves historical data based on your configuration—typically 30-90 days of historical records. Subsequent syncs are incremental, retrieving only records created or modified since the last sync. Sync frequency doesn't affect pricing. You can also trigger manual syncs on-demand via API when needed for specific workflows.
Write operation limitations vary by system. Common limitations include: field-level restrictions (some fields may be read-only), business rule validation (orders may require certain fields or valid vendor codes), permission requirements (the connected account needs specific permissions), timing restrictions (some systems prevent modifications after certain workflow states), and rate limits on write operations. Custom fields in target systems may not be writable through standard APIs. Some systems have transactional requirements—for example, purchase order line items must be created in the same transaction as the order header. During implementation, we identify write operation limitations for your specific use cases and design workflows that work within those constraints.
Makini provides several performance monitoring capabilities. API responses include timing information showing request processing time. The dashboard includes performance metrics showing average response times, throughput, and error rates over time. You can set up alerts for performance degradation or error rate increases. Each request generates a unique request ID that enables detailed performance analysis. For workflow-based integrations, execution logs show per-step timing, helping identify bottlenecks. We recommend implementing client-side monitoring to track end-to-end latency including network time. Monitor trends over time rather than individual requests—occasional slow requests are normal, but sustained increases may indicate issues requiring investigation.
