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Tinybird is a real-time data analytics platform for developers and data teams. Based on the ClickHouse database, it enables users to quickly turn large-scale data into real-time insights and APIs, process data through SQL queries, and build low-latency, high-concurrency REST APIs without the need for complex infrastructure management. the platform supports ingestion of data from multiple data sources, and provides Git integrations, CLI tools, and out-of-the-box testing capabilities. Tinybird emphasizes simplicity and ease of use, allowing developers to create data products in minutes. It's ideal for teams that need to quickly build user interface dashboards, real-time personalized recommendations, or data analytics at scale. The platform offers a free trial program for quick proof of concepts or small production applications.

 

Function List

  • Real-time data ingestion: Supports real-time data import from Kafka, S3, Google Cloud Storage, and many other data sources.
  • SQL Queries and Pipelines: Create reusable data pipelines by writing data processing logic through SQL.
  • API auto-generation: Quickly publish SQL queries as low-latency, high-concurrency REST APIs with Swagger documentation support.
  • Git Integration and CI/CD: Manage data pipelines through Git, support version control and automated deployment.
  • Local Development Support: Provides Docker images and CLI tools to support local testing and development.
  • data security: Supports JWT authentication, row-level security, and multi-tenant access control, and is GDPR and SOC 2 Type 2 compliant.
  • Monitoring and Optimization: Built-in observability UI that provides API latency and data processing statistics to optimize performance.
  • AI Integration: The Tinybird Code is available through Tinybird Code and MCP servers that support direct access to data by AI agents.
  • Data export: Support for exporting query results to Kafka, S3, or Google Cloud Storage.

Using Help

Installation and Initial Setup

Tinybird is a cloud-based platform that doesn't require a complicated installation, but needs to be set up via the CLI or web interface. Below are the detailed steps:

  1. register an account::
    • interviews https://cloud.tinybird.co/If you're not sure what you're looking for, you can sign up with Google, Microsoft, GitHub, or your email address.
    • Select the appropriate cloud region (e.g. US, EU) to create the Workspace.
  2. Installing the Tinybird CLI::
    • Run the following command in a terminal to install the CLI tool:
      curl -sSL https://tinybird.co | bash
      
    • Add the CLI to the PATH environment variable:
      export PATH="/Users/your_username/.local/bin:$PATH"
      
    • Log in to Tinybird:
      tb login
      

      The browser will automatically open to complete the authentication.

  3. local development environment (LDE)::
    • Run a local instance of Tinybird:
      tb local start
      

      This will start Tinybird in a Docker container, suitable for local testing.

    • Create a new project:
      tb create
      

      This will generate a project directory containing the data source, pipeline, and endpoint files.

Data ingestion

Tinybird supports real-time ingestion of multiple data sources:

  • Ingestion from Kafka: Streams Kafka topics directly into a Tinybird data source using the native Kafka connector.
    • Configure the Kafka data source:
      tb datasource connect kafka --topic your_topic
      
  • Importing from S3: Importing files from Amazon S3 is supported with wildcard and timed imports.
    • Example command:
      tb datasource connect s3 --bucket your_bucket --prefix your_prefix
      
  • HTTP event ingestion: Sends data directly through the Events API:
    curl -X POST 'https://api.tinybird.co/v0/events?name=analytics_events' \
    -H "Authorization: Bearer $TB_TOKEN" \
    -d '{"timestamp":"2025-07-25T01:12:00Z","event":"click","user_id":"123"}'
    

Creating a Data Pipeline

The data pipeline is defined by SQL queries and stored in the .pipe file. Here are the steps to create a simple pipeline:

  1. exist pipes/ Create a file in the directory page_views.pipe::
    NODE page_views
    SELECT count(*) as views, page_url
    FROM analytics_events
    GROUP BY page_url
    
  2. Deployment pipeline:
    tb --cloud deploy
    
  3. Publish the pipeline as an API endpoint:
    • Select the pipeline in the Tinybird UI and click "Publish as API Endpoint".
    • Get the API URL, e.g. https://api.tinybird.co/v0/pipes/page_views.jsonThe

Publishing APIs

Tinybird automatically converts SQL queries to REST APIs:

  • Generating APIs: Select the pipeline in the UI, click "Create Endpoint", set the dynamic parameters and authentication method (e.g. JWT).
  • Accessing the API: Use the generated Token Call:
    curl 'https://api.tinybird.co/v0/pipes/page_views.json?token=TB_TOKEN'
    
  • Multi-tenant support: Restrict data access through Row-Level Security:
    SELECT * FROM page_views WHERE tenant_id = {{tenant_id}}
    

data visualization

Tinybird supports integration with tools like Grafana, React, and more to quickly build dashboards:

  • Grafana Integration: Use the ClickHouse plugin to connect to a Tinybird data source and plot real-time charts.
  • React Dashboard: Build dynamic dashboards using the Tinybird API in conjunction with the Tremor component library:
    import { Chart } from 'tremor/react';
    fetch('https://api.tinybird.co/v0/pipes/page_views.json?token=TB_TOKEN')
    .then(response => response.json())
    .then(data => <Chart data={data} />);
    

AI Functions

Tinybird Code allows AI agents to manipulate data directly:

  • Configure the MCP server:
    from agno.agent import Agent
    from agno.models.anthropic import Claude
    from agno.tools.mcp import MCPTools
    tinybird_api_key = os.getenv("TINYBIRD_TOKEN")
    async def main():
    async with MCPTools(url=f"https://mcp.tinybird.co?token={tinybird_api_key}") as mcp_tools:
    agent = Agent(model=Claude(id="claude-4-opus-20250514"), tools=[mcp_tools])
    await agent.aprint_response("top 5 pages with most visits in last 24 hours")
    
  • The AI agent automatically generates SQL queries and returns the results.

performance optimization

  • materialized view: Accelerate aggregated queries:
    CREATE MATERIALIZED VIEW page_views_mv
    TO page_views_summary
    AS SELECT count(*) as views, page_url
    FROM analytics_events
    GROUP BY page_url;
    
  • Monitoring Performance: View API latency and data processing statistics through the Tinybird UI to optimize query efficiency.

application scenario

  1. Real-time User Dashboard
    Organizations need to provide their customers with an interface to analyze real-time data, such as website visits or product clicks.Tinybird quickly generates dashboard data through SQL queries and APIs, supporting highly concurrent access.
  2. Game Data Analysis
    Game developers need to monitor player behavior in real-time, such as level completion rates, and Tinybird ingests the stream of game events to generate real-time leaderboards or personalized recommendations.
  3. E-commerce personalized recommendations
    E-commerce platforms want to recommend products based on user behavior in real time. tinybird ingests clickstream data from Kafka, generates a recommendation API, and integrates it into the front-end.
  4. anomaly detection
    Financial or IoT systems need to detect anomalous events.Tinybird uses SQL to analyze real-time data streams and quickly identify anomalous patterns.

QA

  1. Does Tinybird require a managed server?
    Tinybird is a fully hosted serverless platform, so users don't have to manage servers or worry about scaling. Developers just focus on data processing and API building.
  2. How to ensure data security?
    Tinybird supports JWT authentication, row-level security and multi-tenant access control, is GDPR and SOC 2 Type 2 compliant, and all data is encrypted by default.
  3. Does it support local development?
    Yes, Tinybird provides Docker images and CLI tools for developers to run and test projects locally.
  4. What are the limitations of Tinybird's free plan?
    The free plan provides unlimited use of time and is suitable for proof-of-concept or small applications, but there are limits on the amount of data and the number of queries, which can be found in the https://www.tinybird.co/pricing View.
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