Tinybird is a real-time analytics backend designed for developers and data engineers who need to build and publish high-performance data APIs at scale. It allows teams to ingest streaming and batch data from multiple sources, transform it using SQL, and expose the results as low-latency HTTP API endpoints consumable by applications, dashboards, or other services. Tinybird abstracts the complexity of managing columnar databases and streaming infrastructure, enabling teams to go from raw data to a production-ready analytics API in minutes. It is built on top of ClickHouse, a high-performance columnar database engine, and supports real-time data ingestion via Kafka, HTTP event streams, and S3-compatible object storage. The platform is aimed at product analytics, operational analytics, and customer-facing analytics use cases where query speed and scalability are critical. Tinybird offers a web-based workspace for iterative SQL development, version control via a CLI, and CI/CD integration for deploying data pipelines as code.
Target audience and deployment
- Startup
- SMB
- Mid-market
- Enterprise
- Cloud
- API
Key features
Use cases
- Build real-time analytics APIs
- Ingest and process streaming data
- Power customer-facing analytics
- Develop and iterate on data pipelines with SQL
- Implement operational analytics at scale
Best for
- Data engineers who need to build and ship analytics APIs without managing database infrastructure
- Product teams who need to embed real-time analytics into customer-facing applications
- Backend developers who need to expose high-performance data endpoints at scale
- Data teams who need to iterate on SQL pipelines with version control and CI/CD workflows
Integrations
Developer
GitHub, Vercel
Databases
Kafka, Amazon S3, ClickHouse
Analytics & BI
Grafana, Metabase