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Databricks is a data intelligence platform combining a lakehouse architecture, Spark-based processing, Delta Lake and machine learning tooling. Companies integrate Databricks to unify operational data from ERP, CRM, eCommerce and support systems for analytics and ML, and to serve models and features back into business applications. AIONDATA builds on Lakeflow Connect, Auto Loader, Delta Live Tables, Databricks SQL, the REST API and Unity Catalog governance.
Ready-to-scope pairs with Databricks on one side.
We use Lakeflow Connect managed connectors where they cover the source, Auto Loader for files landing in cloud storage, and custom extractors for APIs such as Shopify, NetSuite or Zendesk. Everything lands in Delta tables under Unity Catalog with lineage, and transformations run as Delta Live Tables or scheduled Jobs.
Yes. Models registered in Unity Catalog can be served via Model Serving endpoints for real-time scoring, or batch predictions can be written back to the CRM, ERP or commerce platform through a sync service. We include monitoring so drift or failed writes are caught early.
Yes. AIONDATA can expose Databricks to AI agents through a custom MCP server that runs governed queries against SQL warehouses, reads Unity Catalog metadata, and calls served models. Permissions follow Unity Catalog grants so agents see only what the calling user is allowed to see.
Yes. The platform is largely consistent across clouds, and we adapt storage, networking and identity integration to each. For Azure customers we frequently combine Databricks with Dynamics 365 and Microsoft Fabric; on AWS we often pair it with S3-based data lakes.
Tell us the other system and what needs to flow between them. You'll get an honest scope and timeline.