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AWS IoT SiteWise is a managed service for collecting, storing, organizing, and monitoring data from industrial equipment at scale on the AWS cloud.
Have any questions? We’re here to help You
Makini supports over 2,000 industrial systems across ERP, CMMS, and WMS categories. This includes major platforms like SAP (ECC, S4/HANA, Business One), Oracle NetSuite, Microsoft Dynamics, IBM Maximo, and specialized industrial systems. We support both cloud-based and on-premises installations. If you need to connect to a system we don't currently support, we're committed to building that integration for you at no additional charge—most new integrations are completed within one business day. You can view our full list of supported systems at makini.io/integrations.
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.
Makini provides sandbox connections for testing without affecting production systems. Sandbox connections include sample data representing common scenarios: standard purchase orders, orders with custom fields, orders in various states (draft, approved, completed), and error cases like invalid vendors or out-of-stock items. Sandbox data is read-only for safety—write operations return success responses without modifying data. This allows thorough testing of your integration logic without risk. For testing with specific systems, we recommend using dedicated test instances of the actual systems (like SAP sandbox environments) connected through Makini, which provides the most realistic testing experience.
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.
