Tinybird is a real-time data processing platform that helps businesses build and deploy data-driven applications quickly and efficiently.
Enhance your data workflows with AI Agents that can access, analyze and act on real-time insights.
Tinybird excels at processing and serving real-time data at scale. Relevance AI transforms this data into intelligent action through AI Agents that can analyze, predict, and respond in real-time.
Instant Insight Generation
Agents can access real-time data for immediate decision-making.
Dynamic Adaptation
Agents can adjust strategies on-the-fly based on live data streams.
Enhanced Predictive Capabilities
Agents leverage real-time analytics for more accurate forecasts.
Relevance AI gives you access to Tinybird's real-time data processing capabilities within your AI-powered workflows.
What you’ll need
You don't need to be a developer to set up this integration. Follow this simple guide to get started:
- A Tinybird account
- A Relevance AI account with access to your project settings
- Authorization (you'll connect securely using API keys—no sensitive info stored manually)
Security & Reliability
The integration leverages secure OAuth authentication with Tinybird's API, enabling authorized data operations through Relevance AI's workflow engine. Built-in request validation and response handling ensure reliable API calls across GET, POST, PUT, DELETE, and PATCH methods.
Automated header management and authentication token handling simplify making authorized requests to Tinybird endpoints.
No training on your data
Your data remains private and is never utilized for model training purposes.
Security first
We never store anything we don’t need to. The inputs or outputs of your tools are never stored.
Best Practices for Non-Technical Users
To get the most out of the Tinybird + Relevance AI integration without writing code:
- Optimize data structure: Use well-defined schemas and appropriate data types for efficient querying.
- Leverage API endpoints: Utilize Tinybird's pre-built pipes and endpoints for real-time data processing.
- Monitor authentication: Keep OAuth tokens current and verify proper account permissions.
- Implement caching: Use materialized views for frequently accessed data to improve performance.
- Handle rate limits: Implement proper request throttling and batch processing for large datasets.
