---
title: "dbt models with a hardcoded table name | Metacenta"
description: "A dbt model that reads a table by its literal name hides the dependency from dbt. What we flag, what we leave alone, and how to route it through ref()."
url: "https://metacenta.com/rules/hardcoded-table-reference"
updated: "2026-10-14"
tokens: 692
---

Lineage integrity · rule hardcoded-table-reference

# dbt models with a hardcoded table name

A Metacenta review checks this under the rule **Models reach inputs through ref() / source()**. Everything below applies whether or not you ever commission one.

## What this rule checks

This rule flags dbt models whose SQL reads a schema-qualified relation, such as `analytics.orders`, instead of using `ref()` or `source()`. A templated schema with a literal table name, such as `{{ target.schema }}.orders`, is flagged too. A bare table name with no schema is not. CTE names, table aliases and system catalogues such as `information_schema` are excluded.

## Why it matters

dbt can't see the dependency, so its DAG is wrong. The model can build before its input, and the reference doesn't follow the target schema between development and production.

## How to fix it

**Replace hardcoded table names so the build order is known.** Replace the literal relation with `{{ ref() }}` when dbt builds it. When something outside dbt loads it, declare it as a source and read it with `{{ source() }}`.

Before:

```sql
select order_id, customer_id, ordered_at
from raw_shop.orders
```

After:

```sql
select order_id, customer_id, ordered_at
from {{ source('shop', 'orders') }}
```

## When it is fine to leave

A warehouse metadata or audit view outside the catalogues we recognise isn't a lineage break, since nothing in the project builds it. Declaring it as a source still records where it comes from. A one-off model due for deletion can also stay as it is.

## What we need to check it

`manifest.json`, with the `raw_code` each model carries. If no model carries it, the rule declines. We read the Jinja-templated SQL as text rather than compiling it, so the finding is medium confidence.

## Published rules it corresponds to

**[dbt_project_evaluator](https://dbt-labs.github.io/dbt-project-evaluator/)**, rule `fct_hard_coded_references`. Ours checks the same condition and threshold.

This means our check corresponds to their rule. It does not mean the publisher reviewed or endorses it.

[Ask about a review](https://metacenta.com/enquire?src=rule-hardcoded-table-reference&via=md)

## Related pages

- [dbt dependency cycles in the model DAG](https://metacenta.com/rules/dependency-cycle)
- [dbt exposures: what they are and how to declare them](https://metacenta.com/rules/model-possibly-unused)
- [Deep dbt lineage: long chains of models](https://metacenta.com/rules/deep-lineage)
- [dbt models with too many direct dependents](https://metacenta.com/rules/fan-out-hotspot)
- [A sample review, as a client reads it](https://metacenta.com/sample)
- [All published rules](https://metacenta.com/rules)
- [How a review is assessed](https://metacenta.com/standard)