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Documentation Index

Fetch the complete documentation index at: https://docs.databar.ai/llms.txt

Use this file to discover all available pages before exploring further.

JQ formulas let you apply JQ queries to any JSON column in your table. The result is written into a new column (stored as JSON), giving you full control over nested data without leaving Databar.
JQ formulas are a transformation — they do not consume any credits.

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What is JQ?

JQ is a lightweight command-line language designed for slicing, filtering, and transforming JSON data. It is particularly powerful when you need to dig into deeply nested objects or arrays that enrichment APIs return. If you are new to JQ, the official manual is a great starting point.

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How to use

1

Add a JQ Formula transformation

Open your table and add a new transformation column. Select JQ Formula from the list.
2

Select the source JSON column

Choose which column contains the JSON data you want to query.
3

Write the JQ expression

Enter your JQ expression. The query runs per row and the result is stored as JSON in the new column.
JQ formula editor with source column and expression input

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Use cases

ScenarioExample expression
Count specific event types[.events[] | select(.type == "purchase")] | length
Extract a nested value.company.funding.last_round.amount
Build a structured output{name: .full_name, city: .address.city}
Filter an array[.contacts[] | select(.role == "CEO")]

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AI assistance for writing JQ queries

A built-in Generate with AI feature is planned to help you craft JQ queries directly inside Databar. In the meantime, any LLM — ChatGPT, Claude, or similar — is excellent at generating JQ expressions. Just describe the JSON structure and what you want to extract.

JSON expander

Extract JSON values into separate columns visually.

Excel formulas

Use spreadsheet formulas for simpler column logic.