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Rays

Plug in your data, ship in minutes

Ingest data over HTTP, from Kafka, S3, GCS, or local files. Queryable in seconds, fully managed, and built for scale.
Try for free

$ ❯ curl -X POST 'https://api.tinybird.co/v0/events?name=my_datasource' \ -H "Authorization: Bearer $TB_TOKEN" \ -d '{"timestamp":"2026-01-01","event":"click","value":42}'

Read the Events API docs

Want to use your favourite language? TypeScript and Python SDKs let you define your Tinybird resources as code.

From source to query in seconds

Tinybird handles buffering, compaction, schema detection, and retries. You focus on your data, not the plumbing.
Tinybird Data Ingestion Architecture Diagram
Rays

Real Ingestion Use Cases

Millions of events per second, zero infrastructure to manage

See how teams ingest data from every source and serve APIs at scale.
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ingests petabytes of data with sub-second latency.

6.45PBdata
ingested
15.27Trows
ingested
<0.2singest
latency

“Our devs evaluated Pinot, Druid and Tinybird. They preferred Tinybird for performance, reliability, integrations, and developer experience.”

Damian Grech

Damian Grech

Director of Engineering, Data Platform at FanDuel

Background

Why Tinybird

Stop building ingestion pipelines

Replace weeks of pipeline engineering with a single platform that handles every data source.

ingestion-plan.md

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# DIY Ingestion Pipeline

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Data in ClickHouse

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Real-time queries

5- Build and maintain HTTP ingestion service6- Deploy and manage Kafka consumers7- Write S3/GCS polling and file parsers8- Implement retry logic and deduplication9- Handle schema changes manually10- Build monitoring for every connector11- Days to weeks to production
1

# Tinybird

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3

Data in ClickHouse

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Real-time queries

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