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Yes, Makini supports multi-region deployments for customers requiring data residency in specific regions or needing high availability across geographies. Each region runs an independent instance of Makini with its own infrastructure, ensuring data remains within the specified region. Multi-region deployments are most common for self-hosted installations where customers want instances in multiple AWS regions or data centers. For cloud deployments, we can discuss region-specific hosting based on your requirements. Multi-region support ensures compliance with data localization regulations and provides geographic redundancy for mission-critical integrations.
Yes, Makini supports both cloud-based and on-premises systems. For on-premises installations, connections require double the connection credits compared to cloud systems. The connection process typically requires opening specific ports and whitelisting Makini's IP addresses in your firewall configuration. For some on-premises systems, VPN tunnels may be necessary. We provide detailed technical requirements during implementation planning. In cases where security policies prohibit external connections, we offer self-hosted deployment options where Makini runs entirely within your infrastructure, eliminating the need for external network access to on-premises systems.
Webhooks allow Makini to notify your application of events in real-time. To set up webhooks, configure a webhook URL in your connection settings or during the initial connection flow. Your webhook endpoint must accept POST requests, respond within 10 seconds with a 200 status code, and use HTTPS with a valid SSL certificate. Makini will send webhook payloads to your endpoint when configured events occur, such as sync completion, connection status changes, or errors requiring attention. We recommend keeping your webhook receiver lightweight—ideally just writing the payload to a queue for asynchronous processing—to avoid timeouts and ensure reliable delivery.
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.
