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Connecting to any system through Makini is straightforward. You select the product you want to connect to, log in with your credentials, and receive an API token. The process takes just a few clicks and works consistently across all 2,000+ supported systems. For most systems (85-90% of cases), you only need the instance URL, username, and password. Some systems may require additional steps like API token generation, and we provide detailed authentication guides for these cases. The connection experience is designed to be simple enough that non-technical users can complete it without IT support.
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.
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.
Yes, through Makini Flows, which includes connectors for popular databases including Snowflake, PostgreSQL, MySQL, MongoDB, and others. This enables workflows that synchronize data between industrial systems and your data warehouse or analytics platforms. For example, you can sync purchase orders from SAP to Snowflake for analytics, or use database queries to drive integration logic. Database integrations use the same workflow builder as other integrations, making it easy to combine industrial system data with database operations. For direct database-to-database syncing, we can help design optimized workflows. Database connections are treated as custom integrations and may require additional workflow development.
