
Data Platform

[01]

Build and iterate on queries in a multi-node editor. Chain SQL nodes together to prototype complex pipelines before deploying.
Generate and refine queries with CMD+K. AI understands your schema and suggests fixes, helping you debug pipelines faster.
See query results as tables. Export data in JSON or CSV to use in external tools or share with your team.
[02]

Track endpoint latency, error rates, and request volumes over time. Visualize any data source, including service data sources like pipe_stats_rt, without writing SQL.
Granularity auto-adjusts to your time range. Drag-to-zoom into any period for a closer look, syncing automatically with the time selector.
Compare metrics side by side with table and chart views. Monitor storage consumption, request patterns, and resource usage across your workspace.
[03]

Use @syntax to reference specific data sources and add workspace rules to fine-tune how the agent responds. The more context it has, the more accurate its answers.
Get improvement suggestions, identify performance bottlenecks, and surface data quality issues. Focus on intelligent analysis, not visualization.
Reasoning nodes created during analysis can be exported to Playgrounds. Continue refining queries in a full SQL environment.
The tools you need
Playgrounds is for prototyping and debugging queries. Write SQL in a multi-node editor, chain nodes together to build complex pipelines, and use AI (CMD+K) to generate or refine SQL. You can inspect results as tables and export data in JSON or CSV.
Time Series is for monitoring endpoint performance and tracking trends over time. Visualize data sources — including service data sources like pipe_stats_rt, endpoint_errors, and datasources_storage — without writing SQL. Granularity adjusts automatically to your selected time range, and you can drag to zoom into specific periods to investigate incidents or observe patterns.
Explorations is an AI agent with full context of your data. Use @syntax to reference specific data sources and add workspace rules to customize how the agent responds. It identifies performance bottlenecks, surfaces data quality issues, and provides actionable insights. Reasoning nodes from the analysis can be exported to Playgrounds for further refinement.
Each tool serves a distinct purpose in the data workflow. Use Explorations to get AI-powered insights and diagnostics, then export reasoning nodes to Playgrounds to refine queries in a full SQL environment. Use Time Series to monitor endpoint performance and track trends over time. The three tools complement each other: Explorations for analysis, Playgrounds for prototyping, and Time Series for monitoring.
Yes. You can keep your Playgrounds, Time Series or Explorations private, or share them with anyone in your workspace. Playgrounds and Time Series configurations persist in your workspace so team members can access and build on each other's work.
All tools work with any data source in your Tinybird workspace, including data sources, pipes, and materialized views. You can also explore service data sources like pipe_stats_rt, pipe_stats, endpoint_errors, datasources_storage, datasources_ops_log, and llm_usage — queries against service data sources are free. In Explorations, use @syntax to reference specific data sources to focus the AI on the right context.
Not necessarily. Explorations lets you query data using natural language. Time Series generates SQL from your configuration choices. However, Playgrounds is designed for SQL users who want full control. Knowing SQL helps you get the most out of each tool.
In Playgrounds, press CMD+K to invoke AI for SQL generation. It understands your schema and suggests fixes, helping you debug pipelines faster. In Explorations, an AI agent with full context of your data analyzes questions, identifies issues, and provides insights. You can export reasoning nodes from Explorations to Playgrounds to continue refining queries in a full SQL environment.

