Data Navigator

Data Navigator helps analysts and marketers explore datasets, apply filters, and extract actionable insights without coding in minutes or seconds.

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About This Tool

Data Navigator enables teams to explore data quickly by loading a dataset reference, selecting relevant fields, applying filters, and generating concise summaries. It accepts CSV, JSON, or SQL-backed sources and returns counts, samples, and basic metrics without requiring code.
Conceptually, the tool treats a dataset as a collection of rows; filters reduce the set, selected columns define the view, and aggregations compute metrics. Outputs include row_count, a representative sample, and computed summaries to support quick decisions.
Who benefits includes data analysts, marketing analysts, product managers, and BI teams seeking faster insight; its unique value lies in no-code data exploration, transparent calculations, multi-source compatibility, and ready-to-export outputs. Use cases include campaign performance analysis, market research, and product analytics where stakeholders need reproducible results.
Core features span dataset reference inputs, validation, multi-criteria filtering, common aggregations (sum, avg, min, max), sample extraction, and export-ready outputs to CSV/JSON for downstream workflows.

How to Use

  1. Provide inputs: supply dataset_uri, format, and an initial list of columns to view.
  2. Define filters: specify boolean expressions and combine with AND/OR.
  3. Configure outputs: select metrics (count, sum, avg) and set a sample limit.
  4. Run navigator: execute to produce row_count, sample_rows, and metrics.
  5. Review outputs: inspect results and export to CSV or JSON if needed.
How to use data navigator tool

Frequently Asked Questions

Find Quick Answers

What data sources are supported?
The tool supports CSV, JSON, and SQL-backed datasets via dataset_uri. It accepts local files or remote databases and handles common authentication methods at the data source level. Validation ensures the requested columns exist before execution.
Can I export results?
Yes. Results can be exported as CSV or JSON, including row_count, computed metrics, and the sample rows. Exports preserve the same filtered view and selected columns used in the session.
How are results computed?
Results are produced by applying filters to the rows, selecting requested columns, and executing aggregations such as sum, average, minimum, and maximum on numeric fields. A representative sample of up to a defined limit is returned for inspection.
How does it handle large datasets?
For large datasets, the tool uses streaming or sampling to avoid full in-memory loads. Users can set a sample size and limit; performance depends on the data source, network, and dataset structure.

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