Query your ingested data

Now your data exists in Tinybird, you can manipulate it and make it extra useful to you.

It's helpful to have an understanding of best practices for using SQL, as this can dramatically improve your request times. Trust us: We know how to run very fast real-time systems at massive scale 🎉


Pipes are a foundational concept in Tinybird, and they're the main way you query your data. Make sure you're familiar with Pipes by reading the Pipes docs and trying out the Playground. You can also follow the step-by-step quick start, too.

Use the Playground

You can find the Tinybird Playground in your Workspace, in the Navigation bar.

Simply put, it's a sandbox for making queries. It allows you to query real time production data, debug queries, and prototype new Pipes without creating any messy or exploration-focused, one-time queries in your Workspace.


If you want to use the Playground to create an Endpoint from one of your Pipes, click the Download button at the top right of the screen and add the .pipe file to your project, iterating as normal using your Git workflow.

Alternatively, you can drag and drop a .pipe file to create a new Playground based on an existing one or Duplicate any existing Pipe from the UI to the Playground.

By default, Playground content is private to your Workspace view. However, you have the option to share your Playground with other Workspace members.

Time series

Time series analysis is a way of analyzing a sequence of data points collected over an interval of time. Use Tinybird's Time Series feature to visualize any time series Data Source in your Workspace, including Service Data Sources. With pipe_stats_rt and pipe_stats, you have visibility and control over all the API Endpoint usage in your Workspace.

Access Time Series via the Time Series tab in the UI (see Mark 1 below):

For more information on how to explore and analyze time series data using Tinybird, read the time series blog post.

Calculate low latency data points

When people talk about "low latency", they mean ingesting, transforming, and retrieving data fast. Depending on the use case, fast could mean a few seconds to milliseconds. ClickHouse (Tinybird’s backend database) is renowned for its exceptional speed of data ingestion, thanks to several key architectural and design principles. Tinybird then takes advantage of Materialized Views, which apply the transformation logic defined in the Materialized View in real time. Finally, Tinybird’s API Endpoints allow you to expose your data in a very quick and easy way. But in order to get a fast response, the Data Source settings and Pipe query logic should be adapted to each low latency use case.

Each use case is slightly different, but the principles are the same. To walk through a fun example, explaining how a hotel booking platform could calculate low latency data points, check out the Low Latency Engine (LLE) example GitHub repo.

Looking for inspiration?

If you're new to Tinybird and looking to learn a simple flow of ingest data > query it > publish an API Endpoint, check out our quick start!