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Makini maintains a comprehensive data model built from analyzing thousands of industrial systems. When data flows through Makini, we automatically transform it from the source system's format into our standardized structure. For example, purchase orders from SAP, NetSuite, and Dynamics all return with consistent field names, data types, and structures. This normalization happens in real-time as data passes through the API. You also have access to raw data if needed for specific use cases. The unified model covers common entities like purchase orders, work orders, inventory items, vendors, and assets, with extensive field coverage across systems.
Yes, you can trigger syncs manually through both the API and the Makini dashboard. The API provides endpoints to initiate syncs for specific entities (purchase orders, work orders, etc.) on a given connection. Manual syncs are useful when you need immediate data updates outside the regular schedule, when onboarding new customers, or when recovering from sync failures. Manual syncs follow the same incremental logic as scheduled syncs, retrieving only changed records since the last successful sync. You can also trigger full re-syncs that ignore the last sync timestamp and retrieve all records within the configured historical period.
Yes, through a combination of sandbox environments, test data, and Makini Flows. For testing different data states, use sandbox connections with predefined test scenarios. For testing system behavior like delays, errors, or specific responses, you can build test workflows in Makini Flows that simulate various scenarios. For testing with actual systems, set up dedicated test instances of your target systems. During implementation, we work with you to identify critical test scenarios and ensure your testing environment supports them. For specific edge cases or unusual system configurations, we can help create custom test scenarios.
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.
