Documentation
Technical References for Platform Integrations
Everything you need to connect your data with trusted enterprise technology stacks and deploy AI workflows for your use cases
Reference guides for the platforms and tools we support
Integration Overview
Prominent's integrations are built to work within your existing architecture—not replace it. Every connection we configure follows secure, standard protocols and is documented so your internal teams can maintain and extend it long after implementation.
Our goal is transparency: you should always know exactly how your data moves and where your AI models run.
Supported Platforms
Snowflake
Connect via secure data sharing and Snowpark for AI/ML workloads, with role-based access control and native data masking.
Databricks
Integrate through Unity Catalog and Delta Lake for governed, high-performance pipelines feeding AI models and analytics.
AWS
Deploy AI workloads using S3, SageMaker, and Lambda for scalable, serverless data processing and model hosting.
Azure
Leverage Azure Data Factory, Azure AI Foundry, and Azure OpenAI Service for enterprise AI integration within Microsoft environments.
How We Approach Every Integration
Discovery & Architecture Review
Understand your current systems, data flows, and security requirements before any connection is made.
Secure Connection Setup
Configure authenticated, encrypted connections using each platform's native security model.
Data Mapping & Validation
Confirm schemas, formats, and data quality align before anything goes live.
Production Deployment & Monitoring
Launch with logging and monitoring in place so issues surface early, not after the fact.
Security & Governance
Every integration is built with enterprise security and governance requirements in mind from day one.
“Good integrations are invisible—your teams keep working the way they always have, just with better data and AI behind it.”
Have a specific platform or architecture question?