# Parabola vs. dbt

> Parabola vs. dbt: dbt is SQL-based transformation in the warehouse for analytics teams. Parabola handles messy operational data and is owned by ops and finance.

Source: https://parabola.io/parabola-vs/dbt

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## TL;DR

dbt models warehouse data in SQL. Parabola handles operational data before it reaches a warehouse.

- **Where the transformation happens:** dbt runs inside the warehouse. Parabola runs on the sources themselves.
- **Language:** dbt is SQL, version-controlled by a data team. Parabola generates each step from a plain-language description.
- **Who owns it:** Data engineering versus the finance or operations team.
- **Where dbt genuinely wins:** Tested, version-controlled analytics modelling at scale.
- **Bottom line:** Pick dbt for analytics modelling. Pick [Parabola](/) for operational processes on messy data.

## Same word, different job

Both tools "transform data," which is why the comparison comes up, and the word hides most of the difference.

dbt's transformations are analytical models: given clean data in a warehouse, express the business logic that turns raw tables into metrics, with tests and version control around it. That is engineering practice applied to analytics, and it works well.

Parabola's transformations are operational: given a supplier's PDF and an ERP export that disagree, reconcile them, apply the rules your team uses, and produce the list of what needs attention. The data has not been loaded anywhere, the logic changes often, and the person who understands it is not a data engineer.

## Parabola vs. dbt at a glance

| Dimension | Parabola | dbt |
| --- | --- | --- |
| Where it runs | On operational sources, before any warehouse | Inside the warehouse |
| How logic is expressed | Each step generated from your description, returned documented | SQL models, version-controlled and tested |
| Inputs | PDFs, emails, portal exports, inconsistent CSVs, ERPs | Tables already loaded |
| Owner | The finance or operations team | Data engineering or analytics engineering |
| Output | Operational results and Artifacts people act on | Modelled tables for analytics |

## Which one is right for your team

Choose dbt when you have a warehouse, a data team, and analytics models worth testing and versioning.

Choose [Parabola](/) when the data has not made it that far, the rules belong to the business, and the output is an operational result rather than a model.