# Parabola vs. Looker

> Parabola vs. Looker: Looker provides a governed semantic layer over warehouse data. Parabola reconciles messy operational data and builds actionable views.

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

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## TL;DR

Looker governs what your metrics mean. Parabola produces the data those metrics are calculated from.

- **The dependency:** A semantic layer is only as good as the modelled data underneath it. Someone has to get the data there and make it correct.
- **What Parabola does:** Reconciles operational sources that were never modelled, applies the business rules, and surfaces the exceptions.
- **What comes out:** An Artifact can be a queue of what needs fixing, not only a governed report of what happened.
- **Where Looker genuinely wins:** Company-wide semantic layers and governed enterprise reporting on consistently defined metrics.
- **Bottom line:** Pick Looker to make the whole company agree on a number. Pick [Parabola](/) to make the underlying data worth agreeing on.

## Governance upstream of the semantic layer

Looker's central idea is worth taking seriously: define metrics once, centrally, and everyone reports the same figures. It solves a real organisational problem, and nothing in Parabola replaces it.

But a semantic layer governs definitions, not inputs. If the operational data feeding the warehouse is incomplete, unreconciled, or silently wrong — a partner file that arrived in a different format, invoices nobody matched — then the governed metric is a consistent view of bad data.

Parabola works on that upstream problem, and it applies its own kind of governance to it: every step shows its input, logic, and output, run history is kept, and row-level data tracing lets you click a figure and see every upstream row that produced it.

## Parabola vs. Looker at a glance

| Dimension | Parabola | Looker |
| --- | --- | --- |
| What it governs | How operational data is assembled, step by step | What metrics mean, across the company |
| Assumes the data is | Messy and spread across systems | Modelled in a warehouse |
| Messy inputs | AI import steps extract from PDFs, emails, and inconsistent CSVs | Out of scope |
| Traceability | Row-level data tracing back through every upstream step | Consistent metric definitions and lineage within the model |
| Output shape | An Artifact: dashboards, exception queues, recommended actions | Governed reports and exploration |
| Who builds it | The ops or finance person, by describing the process | A data team maintaining the model |

## Which one is right for your team

Choose Looker when the problem is organisational consistency — many teams reporting the same metrics from a warehouse a data team maintains.

Choose [Parabola](/) when the problem is that the operational data is not trustworthy yet, or when what the team needs is a queue of exceptions to work rather than a report to read. A common arrangement is both: Parabola reconciling and landing clean data, Looker governing how it is reported.